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U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

CENTERS FOR DISEASE CONTROL

NATIONAL INSTITUTE FOR OCCUPATIONAL

SAFETY AND HEALTH 

+ + + + +

ADVISORY BOARD ON RADIATION AND

WORKER HEALTH 

+ + + + +

SEC ISSUES WORK GROUP

+ + + + +

MONDAY

JULY 28, 2014

+ + + + +

The Work Group convened at the Hotel 

on the Falls, 475 River Parkway, Idaho Falls, 

Idaho, at 1:00 p.m. Mountain Daylight Time, 

James M. Melius, Chairman, presiding.

PRESENT:

JAMES M. MELIUS, Chairman

JOSIE BEACH, Member

GENEVIEVE S. ROESSLER, Member

PAUL L. ZIEMER, Member*
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ALSO PRESENT:

TED KATZ, Designated Federal Official

BOB BARTON, SC&A

NANCY CHALMERS, DCAS*

HARRY CHMELYNSKI, SC&A*

DeKEELY HARTSFIELD, HHS

STU HINNEFELD, DCAS

TOM LABONE, ORAU Team*

JOYCE LIPSZTEIN, SC&A*

ARJUN MAKHIJANI, SC&A

JOHN MAURO, SC&A*

JIM NETON, DCAS

DANIEL STANCESCU, DCAS*

JOHN STIVER, SC&A

TIM TAULBEE, DCAS*

BOB WARREN*

*participating via teleconference 
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T-A-B-L-E O-F C-O-N-T-E-N-T-S

Welcome, Roll Call and Introductions ...... 4

Draft Criteria for the Evaluation and Use of

Internal Exposure Coworker Datasets ....... 8

Evaluation of Differences Between Strata

Coworker Models .......................... 70

Appropriateness of the Model Data for the

Unmonitored Population .................. 100

NIOSH's Reconsideration of the

Application of the OPOS Methodology:

Allowance for Time-Weighted Averaging ... 170

Next Steps and Plans for Board Meeting

Discussion .............................. 223
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1 P-R-O-C-E-E-D-I-N-G-S

2 (1:11 p.m.)

3 MR. KATZ: Good afternoon, 

4 everybody. The Advisory Board on Radiation 

5 and Worker Health. It’s the SEC Issues Work 

6 Group meeting.

7 Sorry for the slightly late start, 

8 but we were trying to get our Live Meeting 

9 situation straightened out, and it should be 

10 now. So people who are on Live Meeting should 

11 be able to see the draft criteria document from 

12 Dr. Neton.

13 We are not dealing with any sites in 

14 particular, really, in this meeting. So we 

15 don't have any conflict of interest matters to 

16 cover before we get going.

17 Let's just do roll call so folks on 

18 the phone know who's in the room and vice versa. 

19 So let's start with the room with our Board 

20 Members.
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1 (Roll call.)

2 MR. KATZ: Okay, then. The agenda 

3 for the meeting, I'm not sure if it's posted yet 

4 or not on the NIOSH --

5 DR. NETON: I think it is.

6 MR. KATZ: It is? Okay. So 

7 that's posted on the NIOSH website -- it's very 

8 simple anyway -- under the Board section of the 

9 website, under today's meetings.

10 And there are a couple of papers 

11 posted there that we're going to be discussing 

12 today. A third paper has too much Privacy Act 

13 protected information to post. So the third 

14 paper will be talked about, but it's not 

15 available to be viewed by the public.

16 And if members of the public want 

17 that in redacted form, they can certainly 

18 request it from me. And we'll provide it in 

19 that case. But it really is -- the reason it's 

20 not redacted and posted is because it's really 
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1 not very useful here, given the extent of the 

2 privacy information. 

3 And Dr. Melius, it's your meeting.

4 CHAIRMAN MELIUS: Yes, okay. 

5 Thank you. And I would just remind the people 

6 in the room and on the phone, when we are 

7 discussing that particular paper, please be 

8 careful. We don't usually have those 

9 situations, but with this one it's necessary.

10 So we're going to start today with 

11 -- essentially we're reviewing the three NIOSH 

12 reports. And we're going to start today with 

13 the first report, which is entitled Draft 

14 Criteria for the Evaluation and Use of Internal 

15 Exposure Coworker Datasets. And Jim, if you 

16 want to start off with your opening monologue 

17 and --

18 DR. NETON: Okay. I'll be happy to 

19 summarize briefly the thinking behind this. 

20 Actually, I noticed, I changed this document. 
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1 And I apparently didn't change the title. 

2 Because it really is for use of Internal and

3 External Exposure Coworker. It's supposed to 

4 be a little more generic than that. But as we 

5 all understand, the internal coworker datasets 

6 are the most difficult to untangle.

7 But anyway, this was one of the 

8 assignments that I've had from the Working 

9 Group meeting -- I think it was a couple of 

10 meetings ago -- was to put out some draft 

11 criteria as to what we would need to consider 

12 to develop coworker models.

13 There's a lot of technical 

14 documents in DCAS that talk about coworker 

15 modeling. But there really was never any 

16 overarching document that sort of put the 

17 requirements, so to speak, on the table.

18 And so this is our attempt at 

19 putting together a -- it's a little more than 

20 an outline. It's certainly fleshed out. But 
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1 it's also far from complete. And so opening 

2 maybe discussions today can help flesh out some 

3 of the concepts that have been put forth.

4 The introduction to this document's 

5 pretty straightforward. It just attempts to 

6 set the regulatory basis of why it's okay to use 

7 coworker models. And that's right out of 42 

8 CFR Part 82, the dose reconstruction regulation 

9 that says if individual monitoring data are not 

10 available or adequate, dose reconstructions 

11 may use monitoring results for groups of 

12 workers with comparable activities and 

13 relationships to the radiation environment.

14 That's a nifty saying, a nice 

15 expression. But, you know, the proof is where 

16 the rubber meets the road. How do you do that? 

17 How do you develop comparable models?

18 In general, we've taken comparable 

19 activities and relationships -- when we discuss 

20 that, we speak in terms of coworker models, 
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1 which we all know what that means after 

2 discussing this for quite some time.

3 But they need to be, in our opinion, 

4 either representative of the workers' 

5 exposures or, and this is important, plausibly 

6 bounding of the dose received by those workers.

7 They don't have to be exact matches. 

8 But they at least have to be able to bound the 

9 exposure experience of the workers. And we can 

10 talk about the sufficient accuracy maybe a 

11 little later.

12 When we're developing these models, 

13 they need to be adequate for the task at hand. 

14 And when it talks about sufficient accuracy, 

15 there's a couple of things that need to be 

16 talked about. Data adequacy is the first one 

17 listed. I'm just going right through the 

18 document.

19 CHAIRMAN MELIUS: And if I can 

20 interrupt, I think what might be useful to do 
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1 is to sort of take this one paragraph at a time.

2 DR. NETON: Okay.

3 CHAIRMAN MELIUS: And get comments 

4 and discussion in that way, rather than going 

5 --

6 DR. NETON: Do you want me to go 

7 back to the --

8 CHAIRMAN MELIUS: No. I think 

9 that's essentially the introduction. But I 

10 think these other sections all have sort of, for 

11 the most part, are individual topics. And I 

12 think that would be helpful, rather than 

13 jumping around. 

14 Because I think we're trying to 

15 decide what needs to be filled in, so to speak, 

16 in these. And I think that would be the most 

17 useful way of doing that. And John, Bob and 

18 Arjun, is that --

19 DR. MAKHIJANI: That's fine. We can 

20 do it that way.
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1 CHAIRMAN MELIUS: Okay.

2 DR. NETON: Okay. So, Section 2 

3 talks about criteria for the evaluation, the 

4 adequacy of the dataset. I mean, clearly, if 

5 the data aren't adequate, they can't be used. 

6 So we've tried to flesh out here a 

7 few of the major concepts of what would be an 

8 adequate dataset that had comparable 

9 activities and relationships.

10 And so the first section on data 

11 adequacy talks about the measurement 

12 techniques. It sort of goes without saying, 

13 but we've always stated that the measurements 

14 that are available have to be able to 

15 quantitatively measure or evaluate the 

16 exposure of the workers.

17 And a good example of this was early 

18 on. It was very recognized that neutron 

19 monitoring, for instance, at many of the sites, 

20 these nuclear track films, couldn't see 
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1 neutrons below a certain energy threshold, 

2 whether it was 500 or 400 keV.

3 So, you know, you couldn't base a 

4 coworker model on that. Or, if radiochemical 

5 analyses were done, were the recoveries 

6 quantitatively sufficient so that you could use 

7 the data? Or was there so much uncertainty in 

8 the chemical recovery of the method that it 

9 couldn't be used?

10 And that's really what this was 

11 talking about here. I'm not sure we're going 

12 to get a lot of discussion on this, but we can 

13 stop there and talk about that.

14 CHAIRMAN MELIUS: The only thing I 

15 would add there is I also think that I would just 

16 add another bullet in there about sort of the 

17 method of collection needs to be appropriate.

18 DR. NETON: Okay.

19 CHAIRMAN MELIUS: And again, 

20 particularly for incident-based, you know, if 
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1 you don't have a reasonably complete set of 

2 collection, that can be -- or inappropriate 

3 timing or whatever. So it's more than just the 

4 method itself or sort of the measurement 

5 method, but also the collection method has to 

6 be, I think, appropriate for --

7 DR. NETON: Okay. I think that is 

8 covered in -- this was really meant to be just 

9 sort of the method, the chemical or analytical 

10 methodology. The program methodology or the 

11 program implementation, I think, is covered 

12 later when I talk about the routine versus the 

13 incident sampling. I get into that later on 

14 when we're talking about the adequacy of the 

15 program itself. I was really just intending 

16 this to be the analytical methodology.

17 DR. MAKHIJANI: Jim, are you 

18 covering quality of data here? By that I mean, 

19 Joyce had raised the question earlier, six or 

20 eight months back, in a discussion about 
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1 Savannah River data where the same urine sample 

2 had been, not two voidings, but the same had 

3 been analyzed twice and yielded quite different 

4 results.

5 DR. NETON: Yeah.

6 DR. MAKHIJANI: Is there a separate 

7 item for that? Or does it belong in --

8 DR. NETON: No. That would 

9 belong, that's an analytical methodology 

10 issue, how robust, I guess, is the methodology 

11 itself. By the way, we've gone through that at 

12 Savannah River, and there's good basis behind 

13 that method.

14 DR. MAKHIJANI: Okay, yeah.

15 DR. NETON: But, yeah. I think 

16 you're right. If you have multiple samples, 

17 and you get widely different results on the same 

18 sample, then you've got an analytical problem.

19 And, you know, there may be ways to 

20 treat that or deal with it. But it would have 
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1 to be addressed. I totally agree with that.

2 DR. MAKHIJANI: Is there kind of a 

3 screen that you've developed for evaluating the 

4 quality of the data under this?

5 DR. NETON: Well, again, this is an 

6 outline. It's not fully implemented. But I 

7 don't know if screen would be the right word. 

8 There certainly are topical concepts --

9 DR. MAKHIJANI: Or a checklist.

10 DR. NETON: Yeah, checklists or 

11 something like that, sure. I mean, that could 

12 be developed as a follow-on to this, for sure, 

13 which would be the more detailed -- I'm trying 

14 to keep the implementation guides a more higher 

15 level document that says here's the major 

16 concepts that need to be addressed.

17 How they're addressed in practice, 

18 I think, tend to be put in more, you know, 

19 procedural type documents or, you know, TIBs or 

20 whatever, something like that.
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1 DR. MAKHIJANI: Yes. But you want 

2 the big pieces in your --

3 DR. NETON: Yeah, I think they're 

4 here. I mean, the quality of the data. You 

5 know, how you go about it though and how you 

6 actually screen or evaluate, I think, would be 

7 the subject of a different --

8 MR. BARTON: This is Bob. I'd like 

9 to make a comment here.

10 DR. NETON: Sure.

11 MR. BARTON: We often talk about 

12 data completeness and data adequacy anytime 

13 we're evaluating a coworker model. And 

14 adequacy, I think, really refers to the science 

15 behind it and how are you making the 

16 measurements and how is that reflected in 

17 actual worker exposures, whereas subjects such 

18 as Awas your monitoring program 

19 incident-based, were you actually capturing 

20 the right people with your monitoring program@
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1 usually falls under completeness.

2 And when you talk about adequacy, 

3 it's somewhat difficult to really kind of get 

4 down into the bones of it. Because every site's 

5 going to have different issues that you might 

6 have to deal with as far as the data quality and 

7 the adequacy of it versus completeness, which 

8 is really looking at the coverage.

9 Aside from whether we can trust 

10 these measurements, are the measurements for 

11 the right people that we want to be able to build 

12 --

13 DR. NETON: Yeah, I agree. I think 

14 this first paragraph would fall under what you 

15 would call data adequacy issues.

16 MR. BARTON: I agree.

17 DR. NETON: I think the next 

18 paragraph starts to get into the completeness 

19 issue, which is do you have enough data? You 

20 know, are there sufficient measurements to 
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1 ensure that the data are bounding and 

2 representative? 

3 You know, we oftentimes get into 

4 this percentage of workers that were monitored. 

5 And I would like to steer clear of a percentage. 

6 Because, as I try to point out in here, there 

7 are programs, like at Savannah River, where 

8 only 15 people were working on some operation 

9 with some exotic radionuclide.

10 And, yeah, there's 10,000 people at 

11 Savannah River, but that doesn't -- it's not a 

12 really good indication of the completeness of 

13 the monitoring. Because it's the completeness 

14 of the exposed population that needs to be 

15 addressed.

16 And, you know, a good example here 

17 is the National Laboratory. They have a lot of 

18 different experiments with a wide variety of 

19 nuclides. But not everybody was exposed to 

20 those nuclides.
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1 That brings into play a whole 

2 different issue, which is how do you apply those 

3 coworker models to those little pockets of 

4 individuals.

5 MEMBER BEACH: How do you identify 

6 who fits in the --

7 DR. NETON: Yeah, and that's 

8 something I would actually like to discuss in 

9 some more detail. You know, in the past, I 

10 think we've been able to say, well, we'll apply 

11 it to everybody. But I'm not 100 percent 

12 certain that that is appropriate either.

13 If you have 15 people that were 

14 exposed, and you've got 1,000 potentially 

15 exposed workers, it doesn't seem to me to be 

16 appropriate to say, okay, I'm going to give all 

17 1,000 workers the exposure that probably only 

18 15 people received. So I'm not sure how that 

19 plays out.

20 CHAIRMAN MELIUS: Well, that's a 
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1 section I think needs to be developed more. 

2 DR. NETON: Yes, I agree.

3 CHAIRMAN MELIUS: And it's sort of 

4 just taking a look at what is available. In two 

5 ways: what is available and then what holes are 

6 we trying to fill? So again, you know, what 

7 data's available? How does it break down by, 

8 you know, building, and task, and type of work, 

9 and process involved and so forth, so you have 

10 a good idea of how wide that coworker model 

11 might be or how many parts there are to it or 

12 who might be included and who's not.

13 But I also think a second part of 

14 that is what gaps are you trying to fill? And 

15 are those, you know, gaps -- because, 

16 essentially, the bigger the gap you're trying 

17 to fill, if you have 15 people, or 100 people, 

18 whatever, and you have only monitoring from one 

19 year and you have monitoring, you know, 20 years 

20 later, well, what happened in those intervening 
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1 18 years is a bigger gap to predict with a 

2 coworker model than if, well, occasionally 

3 somebody's missing or there's a year where you 

4 have some problems with the laboratory or 

5 something, you know, where you can't use the 

6 data and so forth.

7 And, well, you've got good data on 

8 both sides of it and so forth. But that's, I 

9 think, a different question. And I think it's 

10 also a different statistical question of what 

11 you're trying to predict.

12 DR. NETON: I agree, I agree.

13 CHAIRMAN MELIUS: And I don't think 

14 there's hard and fast rules for doing that. But 

15 I think you have to take and examine the data, 

16 and array it and look at it with some process 

17 to how you would -- and documentation of what 

18 you're doing.

19 And I think that's some of what we've 

20 been missing in terms of what we see. It may 
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1 very well be done or, you know, it may be done 

2 appropriately. But I'm not sure. But then 

3 when it comes down to it, there's got to be sort 

4 of a strategic decision of where does the 

5 coworker model work and, you know, be feasible 

6 or not feasible in terms of what we're trying 

7 to do.

8 DR. NETON: I think we've kind of 

9 done that as we go through these deliberations 

10 on like Savannah River, you know. But it would 

11 be better to have done it up-front. I totally 

12 agree.

13 CHAIRMAN MELIUS: Yeah, yeah. I 

14 just don't know if we've always done it 

15 consistently.

16 DR. NETON: Exactly.

17 CHAIRMAN MELIUS: We may have, may 

18 not have. And part of the reason we might not 

19 have done it consistently is because we're still 

20 wrestling with how to do it.
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1 MEMBER BEACH: Well, some of the 

2 sites that come to mind, and I don't know if this 

3 pertains, but the Oak Ridge, the hospital, I 

4 mean, we gave it -- we did the full, you know, 

5 the full grouping. Because we didn't know the 

6 handful --

7 DR. NETON: Right.

8 MEMBER BEACH: So it's important 

9 that we get this right.

10 DR. NETON: Exactly. That's a good 

11 example, Josie, yes.

12 MEMBER BEACH: There's been a 

13 couple of good examples that we've given --

14 DR. NETON: I tried to do that when 

15 I was putting this together, is going through 

16 it and looking at how we've behaved in the past. 

17 And I think we've been somewhat consistent.

18 But we've never started from a 

19 common point, like here, where we said, okay, 

20 let's go here, here, here and here, almost like 
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1 Arjun was just talking about, that sort of 

2 checklist almost where, you know, we start from 

3 there.

4 MEMBER BEACH: But there's another 

5 site that comes to mind that was one building 

6 and we gave it to the whole facility. And it 

7 was just a couple of years ago. I can't think 

8 of -- no, it wasn't Mound.

9 CHAIRMAN MELIUS: There was Linde, 

10 where we had --

11 MEMBER BEACH: No. It wasn't Linde 

12 either, because --

13 CHAIRMAN MELIUS: We had Fernald 

14 where there were --

15 MEMBER BEACH: It wasn't any of 

16 those main sites. It was just -- it was a 

17 different --

18 DR. MAKHIJANI: Yes. There was a 

19 place where --

20 (Simultaneous speaking.)
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1 DR. NETON: Blockson? Blockson 

2 became an SEC, because the radon model wasn't 

3 sufficient. But I think AWEs are good examples 

4 though. Bethlehem Steel, even though it's an 

5 SEC, it still has dose reconstructions done for 

6 uranium that is the same dose for every single 

7 person, every single claimant.

8 MEMBER BEACH: I guess my point is 

9 if this is important it's going to be 

10 challenging, obviously. We've been struggling 

11 with it for a couple of years.

12 DR. NETON: Yes.

13 DR. MAKHIJANI: Yes. But 

14 Bethlehem Steel and Mound, I guess, were a 

15 couple of different examples. Bethlehem 

16 Steel, there was no way to identify who was in 

17 that rolling mill, right? Wasn't that --

18 DR. NETON: Well, correct. But you 

19 can make the same argument for, again, to go back 

20 to Savannah River. I've got 15 people that 
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1 worked on a neptunium encapsulation project. 

2 If I can't identify it, then can I reasonably 

3 identify who was in that area? Now, I don't 

4 want to get into specifics on issues with badges 

5 and access and entry. But that's what we're 

6 trying to do here.

7 And my opinion is if you can 

8 demonstrate with some confidence that you can 

9 bound the work, you know which workers were in 

10 those areas, then, yeah, you could say I want 

11 to apply to these workers that had access to this 

12 building during this year. And I think that's 

13 okay.

14 DR. MAKHIJANI: Well, then you deal 

15 with a lot of other issues that go away too in 

16 terms of comparison.

17 DR. NETON: Yeah.

18 DR. MAKHIJANI: Because if your 

19 universe of worker is fairly uniform, then a lot 

20 of issues go away.
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1 CHAIRMAN MELIUS: But we've also 

2 talked about sort of a nightmare issue of what 

3 happens if -- do we get to the point where we 

4 do an SEC for an individual worker? Because 

5 that individual worker, he just doesn't fit 

6 whatever models we have and is somewhat, you 

7 know -- and we don't have adequate data. And 

8 you're not going to identify that worker until 

9 you get to the point of doing the individual dose 

10 reconstruction.

11 And that's a tough issue. Because, 

12 again, that was one site where I had mentioned, 

13 and I think Stu and Jim ran out of the room --

14 (Simultaneous speaking.)

15 DR. NETON: Let's think about how we 

16 actually behaved in situations like this. 

17 Thorium has been sort of the poster child for 

18 adding SECs, right because it's almost 

19 impossible to monitor, at least on a personnel 

20 monitoring basis, with sufficient accuracy. 
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1 It's hard, but not impossible.

2 But very often, we've found 

3 instances where thorium was used on a fairly 

4 limited basis and made the entire site an SEC 

5 because we don't know who was in that area. And 

6 it's likely that a small fraction of the 

7 workforce was exposed to thorium. So that 

8 precedent has sort of been set.

9 DR. MAKHIJANI: Yeah. I think 

10 between thorium and Bethlehem Steel you have a 

11 fairly clear precedent that if you really can't 

12 identify you've got to do the whole site.

13 Now, between that and, say, in Mound 

14 you actually had, you know, the tritides. 

15 Didn't you initially start out with the idea 

16 that there was a specific group of people, and 

17 then it turned out that it was very fuzzy at the 

18 edges?

19 MEMBER BEACH: Yes.

20 DR. MAKHIJANI: And it became more 
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1 difficult. I wasn't too involved but that's sort 

2 of my vague memory.

3 DR. NETON: Actually, in Mound we 

4 ended up reconstructing, because we had the --

5 MEMBER BEACH: They got it for 

6 radon.

7 DR. NETON: Right. The Mound was 

8 tritides were reconstructable because we had a 

9 lot of smear data and surface contamination 

10 measurements that allowed us to bound it.

11 CHAIRMAN MELIUS: But I think why 

12 this document is important, why I wanted it 

13 first, is that I think what we learned from 

14 thorium was that it wasn't as easy as saying, 

15 well, just every thorium site should be an SEC.

16 DR. NETON: That's true.

17 CHAIRMAN MELIUS: We found ones 

18 where we can do dose reconstruction. And 

19 there's also issues of, well, how much exposure 

20 was there even, you know, at the extreme of given 
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1 what's happening at that site.

2 So I think it's more been determined 

3 by the facility, you know, all the individual 

4 factors. And I think if we can achieve, through 

5 this kind of a document, eventually get to the 

6 point where we have sort of a process that's 

7 consistent and at least will identify where 

8 coworker models make sense to do, when shouldn't 

9 we even try, or how do we then set up those 

10 coworker models, or for what group.

11 MEMBER ZIEMER: Dr. Melius?

12 CHAIRMAN MELIUS: Yes, Paul, go 

13 ahead.

14 MEMBER ZIEMER: Yeah. I just wanted 

15 to raise sort of a general question at this 

16 point, because we’re getting into a lot of 

17 specifics here that are site-oriented.

18 But it seems to me that, and let me 

19 ask the question, isn’t this document intended 

20 not to be very prescriptive, but more almost 
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1 philosophical on what issues have to be 

2 considered? And then for each site you would 

3 have to answer the question, have you met sort 

4 of the broad-brush criteria? I mean, how much 

5 specificity?

6 I’ll ask Jim Neton first. Because in 

7 terms of reading this document, it seems to me 

8 it's currently fairly broad, and maybe that's 

9 the way it should be. There are a lot of details 

10 built into each given site, into each of the 

11 sentences. But it doesn't seem to me you'd want 

12 the specificity in this document that would 

13 cover all cases.

14 DR. NETON: Well, you're right, Dr. 

15 Ziemer. I intended this to be fairly general, 

16 you know.

17 MEMBER ZIEMER: Yeah, that was my 

18 point. And a lot of the questions we raise very 

19 specific to certain situations and sites, which 

20 you would have to answer on an individual basis, 
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1 but you certainly can't cover it in that kind 

2 of detail in this sort of document.

3 DR. NETON: But at the same time, I 

4 guess, I kind of feel that, given what we've 

5 learned from the past, we might be able to 

6 incorporate some guidance in here that is 

7 helpful.

8 For example, we just talked about 

9 the thorium and why haven't all sites gone SEC 

10 just because thorium was there. And the 

11 thought occurred to me is it has to do with the 

12 extent of the spread of contamination, or the 

13 possible extent of the spread.

14 So, you know, one could put in here 

15 a little bit of verbiage about, you know, how 

16 widespread -- these are not going to nail it down 

17 specifically, but --

18 CHAIRMAN MELIUS: I just want to 

19 make -- Paul, I'm just trying to make sure we 

20 have all -- I won't say all -- most of the factors 
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1 that need to be considered included in here. 

2 They don't need to cover the specifics for every 

3 site, but that all these factors that need to 

4 be considered in deciding to do and then 

5 developing a coworker model get included. And 

6 so we --

7 MEMBER ZIEMER: Well, I understand 

8 that, Jim. For example, let's just take a 

9 sentence that says you have to have adequate 

10 calibration methods. Well, add to that a 

11 paragraph of the kinds of things that have to 

12 be considered. I mean, there all kinds of 

13 issues around each of these. So how much 

14 specificity are we talking about?

15 CHAIRMAN MELIUS: Well, I think we 

16 need to, you know, as we go through this, point 

17 out where we think more specificity would be 

18 helpful. And I would agree with you on 

19 calibration. We don't want to have this have, 

20 you know, a 200-page textbook on all the 
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1 calibration methods that are out there.

2 MEMBER ZIEMER: Yeah, or a checklist 

3 or something like that.

4 CHAIRMAN MELIUS: And I'm probably 

5 underestimating the number of pages.

6 (Laughter.)

7 CHAIRMAN MELIUS: So I agree with 

8 that. I think there are other areas where we’ve 

9 -- part of the problem we have when we're 

10 wrestling with these coworker issues is that we 

11 haven't had assurances that we're seeing all the 

12 same information that's important, that we're 

13 not missing something.

14 MEMBER ZIEMER: Right. If we can 

15 identify what the issues that have to be 

16 grappled with and maybe whatever level they have 

17 to go on it. But, yeah, I was just concerned 

18 that we're starting to discuss specific sites 

19 and getting way down into the weeds here.

20 CHAIRMAN MELIUS: Yeah. Well, I 
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1 don't think we're trying to settle any specific 

2 sites. I think it's only --

3 MEMBER ZIEMER: Yeah, I know we're 

4 not trying to settle them. I got the idea you 

5 were trying to get that much detail into the 

6 document. It's got to be somewhere above that.

7 If we can identify, for example, the 

8 thorium issue, as Jim suggested, probably needs 

9 to be addressed in some way, in a broad way, to 

10 make sure that it’s handled always and 

11 consistently. And same with other issues of that 

12 type.

13 DR. NETON: I was just wondering if 

14 maybe this is the spot that an appendix to the 

15 Implementation Guide that had sort of some 

16 checklist points in it. You know, not fleshed 

17 out in detail but, you know, each sentence --

18 not each sentence -- but any sentence where it 

19 seemed warranted, one would sort of say such as, 

20 you know, items to be considered, et cetera, you 
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1 know, just four or five things, just to give it 

2 a little more guidance there but not getting 

3 into the specifics of it.

4 But say, you know, you should also 

5 -- some of these sentences do beg for some 

6 expansion maybe just to give some examples of 

7 what the sentence is referring to. I've done 

8 that a little bit in here, but I haven't gone, 

9 certainly, extensively into it.

10 CHAIRMAN MELIUS: And I would just, 

11 you know, do something simple like adding 

12 paragraph numbers or something. So, you know, 

13 you have 2.1, but it would be 2.11 and 2.12. And 

14 label each of these paragraphs so that --

15 MEMBER ZIEMER: Yeah, I think that 

16 would be a good way to do it. Either that or 

17 have an appendix where you expand it 

18 appropriately for each item. For example, if 

19 you had some calibration specifics that need to 

20 be considered across the board, you embed that 
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1 in an addendum or something like that.

2 CHAIRMAN MELIUS: Or refer to other 

3 existing documents, there may be existing 

4 documents.

5 MEMBER ZIEMER: Or existing 

6 documents, right.

7 CHAIRMAN MELIUS: Okay.

8 MR. BARTON: Yeah. This is Bob. I 

9 think it's going to be very difficult to be 

10 prescriptive when it comes to all these 

11 different sites. I mean, that's why they have 

12 these meetings. That's why we have Site 

13 Profiles, because you’re going to encounter 

14 different issues depending on what’s happening 

15 where.

16 But also, I guess in the general 

17 sense, I’d like to reply, Jim, to your comment 

18 about you sort of gave the example of where you 

19 have a site with thousands of workers. But, you 

20 know, maybe you only have 15 workers who are 
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1 working with a specific exotic radionuclide.

2 So, really, your exposure 

3 potential's pretty much restricted to that 

4 small group. But I think in the context of this 

5 program, I think that a really high bar has to 

6 be set to actually exclude them and say you 

7 couldn't have been exposed.

8 Now, at certain sites that's, you 

9 know, evident, if you have access registers 

10 where they simply couldn't have entered the 

11 facility. That would be a very powerful piece 

12 of evidence. And it's going to vary from site 

13 to site. But I just wanted to make that comment 

14 that I think a very high bar has to be set if 

15 you're going to unequivocally state that they 

16 weren't exposed and that's why we're not going 

17 to be applying a given coworker intake.

18 DR. NETON: I agree. And we 

19 oftentimes run into a situation where we know 

20 exactly who worked with the material. These 12 
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1 people were on this list. But then you always 

2 run into a situation where, well, what about the 

3 maintenance folks who were in there, and the 

4 janitorial type staff or, you know, cleanup 

5 people? You know, clearly you had to 

6 decommission that at some point, so how do you 

7 deal with those people?

8 And that's when we -- well, I won't 

9 say it falls apart, but it's harder to justify 

10 then that only these 12 people trying to get 

11 assigned to that.

12 MEMBER BEACH: It definitely 

13 falters at that point.

14 DR. NETON: On the other side of the 

15 coin, though, to get back into the coworker 

16 model arena, you know, you have only 15 workers 

17 who were potentially exposed, and they were the 

18 ones working with the material full time. You 

19 know, is that model applicable then to these few 

20 other workers who came in on sort of a 
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certified by the Chair of the SEC Issues Work Group for accuracy at this time. The reader should 

be cautioned that this transcript is for information only and is subject to change.

40

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1 miscellaneous basis? I mean, that's another 

2 question. 

3 CHAIRMAN MELIUS: And is there 

4 another model for them? That's, I think -- you 

5 know, in some ways, for efficiency purposes, 

6 we've tended to try to keep this, you know, 

7 simple. And I understand that. But, you know, 

8 if we know there’s these 12 workers, you know, 

9 and they have a certain range of exposures or 

10 whatever --

11 MEMBER BEACH: But doesn't that 

12 triangle effect that we were dealing with, I 

13 think, with GSI, where a certain percentage got 

14 this, and the next level got a certain point and 

15 then the lower -- we've done that. Would 

16 something like that apply here or in a coworker 

17 model situation?

18 DR. NETON: Well, yeah, but you have 

19 to define who falls into each of those three 

20 groups.
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1 MEMBER BEACH: Right, I realize 

2 that. 

3 DR. NETON: That's the difficult 

4 part.

5 MEMBER BEACH: It's complicated.

6 MR. STIVER: And these tiered 

7 models are always difficult to use, to classify 

8 people by exposure potential of a job type.

9 DR. NETON: My only answer to that 

10 is that I feel we've been extremely 

11 claimant-favorable in those regards. But 

12 that's also subject to interpretation.

13 MEMBER ROESSLER: I think the 

14 danger in all of this, or the downside, is that 

15 we could be severely overestimating doses for 

16 a lot of people. And that's not a realistic or 

17 representative thing. I think that we have to 

18 keep that in mind as well as not accounting for 

19 exposures that people get.

20 DR. NETON: We always have to keep 
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1 in mind, it’s easy to think about who was exposed 

2 and what their exposures were, but you also have 

3 to keep -- I think it's in here somewhere, I hope 

4 it is -- that you're reconstructing doses of 

5 people who weren't monitored.

6 And to the extent that you can define 

7 why they weren't monitored and show their 

8 exposure potentials were either limited or 

9 non-existent, then it's a different ballgame.

10 You know, the way it’s sort of a 

11 priori right now is we’re assuming that anybody 

12 that's unmonitored, unless it can be proven 

13 otherwise, had a pretty high potential for 

14 exposure. I mean, that's the way we've been 

15 working it. And I'm not sure that's the right 

16 way to go. I mean, that's the way we've been 

17 doing it. Because there are many cases where 

18 people weren't monitored for very good reasons. 

19 And it's a hard thing to demonstrate though. 

20 I think that'll come up later. 
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1 We're getting maybe a little bit off the subject 

2 of this one paragraph. I think that the next 

3 paragraph, I think, is going to be the subject 

4 of a little bit of discussion. 

5 This talks about -- I tried to put 

6 in something about the minimum number of samples 

7 required to be available for a model --

8 actually, one interval of the model. Like if 

9 you have one year, one quarter. And this was 

10 in RPRT-55. So it's nothing new. But it 

11 seemed to me that you need to specify some 

12 minimum. And here we put 30 in here. And that, 

13 in the context of 30, would mean 30 individuals 

14 with monitoring data, not 30 samples. Because 

15 that's another issue we need to talk about it: 

16 is this individuals or individual samples?

17 But in the way we're thinking to run 

18 this, it will be 30 individuals. But that's 

19 flexible too, because if you have the universe 

20 of all monitored people is 15, then it is what 
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1 it is. But if you had a large cadre of workers 

2 that was much more than 30, and you had a minimum 

3 of 30 that were somewhat representative, I think 

4 that seemed to be a fairly decent number.

5 Although the dose reconstructor or 

6 person developing the model would certainly 

7 have some leeway, you know, to deal with special

8 situations. That's what I had in mind here.

9 MR. BARTON: And just to add on to 

10 that, because you actually do say it later in 

11 your paper, I'm not sure what exact page it is, 

12 but you mention the fact that, you know, when 

13 we're looking at a time interval, you can't just 

14 consider the number of samples you have in the 

15 time interval. You have to consider what 

16 campaigns were going on, you have to consider 

17 the air sampling to make sure that, when you 

18 choose an exposure regime -- I guess, you know, 

19 that's term we could sort of use for it -- that 

20 those people who are included that period, 
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1 normally we say it’s a year, but in fact it may 

2 not be a year.

3 DR. NETON: Yeah, yeah.

4 MR. BARTON: You might want to get 

5 the campaigns and whether there was a change in 

6 exposure potential. Say they started a 

7 campaign in July, it ran through June of the next 

8 year, it really might not be appropriate to 

9 average each of those individual years but 

10 rather look at the campaign interval. So while 

11 I see what you're saying there --

12 DR. NETON: That's why we like the 

13 OPOS. But that’s a different story.

14 (Laughter.)

15 DR. NETON: Sorry.

16 MR. BARTON: But I was just saying, 

17 when you choose a time interval, I think it's 

18 important not just to look at the number of 

19 samples you have in a given interval but --

20 DR. NETON: Yeah, and I was just 
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1 saying that certainly a coworker model with five 

2 samples doesn't seem to me to be a very valid 

3 coworker model, unless there were only five 

4 people that worked with that material and that 

5 was all there was.

6 But 30 seems to be a good number. I 

7 know there's been some discussion on this in the 

8 past, about where it comes from and, you know, 

9 the central theory and all that.

10 But, you know, anyway, I feel it's 

11 appropriate at least to have some minimum number 

12 in there. But it's not a hard and fast rule. 

13 That may be not one of our major points of 

14 contention after all.

15 CHAIRMAN MELIUS: I would just sort 

16 of expand a little bit on what Bob was saying 

17 a little bit, I think what is important, 

18 probably more important, is it's not the number 

19 but sort of the circumstances at the site and, 

20 you know, looking at both what happened at the 
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1 site, what were the nature of the exposures, 

2 what's the range within that group of workers? 

3 You know, was there something else going on? Or 

4 can you differentiate in some way among those 

5 that might make a difference in terms of your 

6 coworker model?

7 And then the other side of it is what 

8 gaps are you trying to -- how big are the gaps 

9 you're trying to fill?

10 DR. NETON: Right.

11 CHAIRMAN MELIUS: And there may be 

12 times when, you know, having ten people and you 

13 know the process was very stable, didn't change 

14 over time, and you have ten people monitored for 

15 a number of years, that may be adequate for a 

16 large group.

17 And you look at the history of that 

18 particular area or whatever, that that's been 

19 relatively stable throughout the time that it 

20 was monitored and the monitoring was 
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1 appropriate.

2 So I think getting people to look at 

3 that and sort of getting that into the decision 

4 process is as important as the 30. Yeah, you're 

5 not going to do it with three samples or five 

6 or whatever if it's a huge number and, you know, 

7 a fair amount of variability.

8 DR. NETON: This really comes into 

9 play usually when we have what we would call 

10 exotic radionuclides, and, you know, small 

11 amounts of workers dealing with curium, 

12 californium, something like that.

13 But I agree. And the final sense of 

14 this one I think is -- I don't know if it needs 

15 to be expanded on -- but, in my mind, it's 

16 extremely important. It speaks to like the 

17 validation effort.

18 If you've got an electronic database 

19 on the site or some summary records, and you're 

20 using that to develop your model, it should be 
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1 reviewed against some representative sampling 

2 of the original data to demonstrate that the 

3 pedigree is okay, that you've got -- that's 

4 sometimes harder to do than others. But to the 

5 extent one can do that, you know, we've gone to 

6 the point where at some point you have summary 

7 data and you get the original log sheets that 

8 say, well, there's 1,500 samples that the lab 

9 said they processed in this month in >53. And 

10 lo and behold, you've got about that number in 

11 your bioassay records. And it gives you a good, 

12 comfortable feeling that you're not dealing 

13 with something that's just totally, you know, 

14 out of joint.

15 So I don't think there's going to be 

16 too much argument. To the extent, I guess, that 

17 we do this, is subject --

18 CHAIRMAN MELIUS: Is subject to a 

19 great deal of argument. Unfortunately, 

20 because it can be very cost --
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1 DR. NETON: It can be very cost 

2 prohibitive. I mean, because we've been there. 

3 I could think of various sites where, at Rocky 

4 Flats, we were just comparing the data sheets 

5 and some values. The representative sampling 

6 seemed to be okay. I certainly don't think we 

7 have to go and do them all. I mean, that's not 

8 --

9 MEMBER BEACH: No, a sampling set or 

10 something.

11 DR. NETON: Yeah, a subset and just 

12 look at, just to give yourself a comfortable 

13 feeling that the dataset you have is complete, 

14 that it represents something.

15 And even if it's not 100 percent 

16 complete, it's not missing data that would bias 

17 your model, you know, one direction or the 

18 other. Or all the incident samples are in a 

19 drawer somewhere, you know, that kind of thing.

20 CHAIRMAN MELIUS: Yeah. I would 
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1 say either in this document, or in maybe another 

2 document, you may want to sort of do a procedure 

3 for that or something that would -- anything 

4 that would, you know, capture a lot of the same 

5 things that we've talked about in this section 

6 already.

7 But I think one of the problems we 

8 have is that we tend to do an inadequate job. 

9 And then we argue as to whether that inadequate, 

10 that limited -- say we do a very limited job, 

11 a quick look. It looks okay. Then we argue 

12 about, well, is that representative? And we 

13 didn’t put a lot of thought into the original 

14 one, because it's a sample of convenience.

15 DR. NETON: Right.

16 CHAIRMAN MELIUS: And then we try to 

17 figure it out. And meanwhile, we often have 

18 complaints from the workers that, you know, 

19 their data's missing, or whatever, something 

20 was missed or whatever.
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1 And I think having some better, 

2 agreed upon process for that would be very 

3 helpful and would avoid a lot of problems for 

4 the rest of us, except for Stu who has to come 

5 up with a budget to --

6 MR. HINNEFELD: Well, I don't have 

7 any trouble coming up with a budget. They tell 

8 me what it is.

9 (Laughter.)

10 MR. HINNEFELD: Then we just work 

11 until we're out of money.

12 MR. STIVER: I'd say that 

13 historically it’s kind of been driven by, you 

14 know, by the economics, really, but also the 

15 criteria for an SEC determination was a little 

16 bit, at least historically, has been a little 

17 more stringent.

18 And so we haven't done, at least from 

19 SC&A's perspective, we haven't done the really 

20 in-depth data adequacy and completeness 
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1 analyses for each Site Profile review that we've 

2 done. And so this can come back at a later date 

3 when an SEC Petition is filed. And then we have 

4 to go back and say why didn't you do this, and 

5 that and so forth.

6 DR. NETON: My recollection is 

7 they're all very thorough.

8 (Laughter.)

9 DR. NETON: You know, we're talking 

10 about a lot of things. I'm hoping other folks 

11 can help me take down some minutes. Because 

12 it's not possible for me to think, and write and 

13 talk at the same time.

14 (Simultaneous speaking.)

15 CHAIRMAN MELIUS: Well, we’ll have 

16 the transcript.

17 DR. NETON: I agree, the transcript 

18 is the gold standard. But I have a feeling that 

19 time is of the essence with this stuff.

20 MR. BARTON: If I could comment, 
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1 this last sort of concept we were talking about, 

2 that might be one place where we could get a bit 

3 more prescriptive. Because what we would be 

4 talking about, essentially, is you have an 

5 electronic database of some sort. And then you 

6 have records, a sample of records where you can 

7 compare it against what would be an acceptable, 

8 I guess you'd call it an error rate, or what is 

9 an acceptable percentage of missing records that 

10 would obviate or would not obviate a certain 

11 coworker model?

12 And then you also mentioned, and I 

13 think it's very important, missing records that 

14 we do uncover, what effect would they actually 

15 have on a coworker model? As was mentioned, you 

16 know, if you're missing all the incident samples 

17 that could be a major problem, whereas the actual 

18 percentage of missing records might not even 

19 matter anymore because you could be just missing 

20 the sort of upper percentiles.
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1 So I think, you know, maybe a 

2 literature review of some sort, we could 

3 actually sort of get a more prescriptive 

4 approach that would apply across all these 

5 different sites.

6 Because, again, we're just comparing 

7 sort of an electronic database forming the basis 

8 of the coworker model versus whatever available 

9 hard copy records we have.

10 DR. NETON: Well, that's the 

11 problem, though. The hard copy records that are 

12 available are not uniform. I mean, you can't 

13 predict. So in some cases, we have numbers of 

14 samples taken by months, some places we have 

15 actual laboratory notebooks.

16 So it depends. And I guess, you 

17 know, can you not build a the coworker model, 

18 then, if you don't have the gold standard to 

19 compare your dataset against? 

20 CHAIRMAN MELIUS: Yeah. But I 
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1 think there'd be a way of looking at that that's 

2 not so intensive or extensive that it would --

3 I think the hardest one is the incident issue.

4 Because, again, that's one where you 

5 usually have, you know, people claiming there 

6 were incidents, and not being able to find the 

7 monitoring records and lots of reasons for that, 

8 both good and bad. And so we need to -- but for 

9 sort of routine sampling, you're right, they're 

10 not uniform.

11 And it's not just, you know, taking 

12 a random sample of 30 out of, you know, 20,000 

13 or whatever. It's something, you know, more by 

14 year and making sure certain areas are covered 

15 and so forth. But that's all, you know, pretty 

16 straightforward statistics to do. And I think 

17 it could be done. Famous last words.

18 DR. NETON: I guess we all agree that 

19 some more guidance is required here. Whether it 

20 goes into this document or into a separate one, 
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1 you know, I don't know if it should fall under 

2 a checklist or -- probably not a checklist. 

3 This would be more of a philosophical, we do you 

4 really need to prove completeness.

5 DR. MAKHIJANI: Well, the incident 

6 thing is very difficult. Because, you know, as 

7 Jim was saying, workers claim they were in 

8 incident, especially like construction workers 

9 who weren’t there all the time and may not have 

10 had the same level of health physics coverage 

11 because they might have been feeling they're in 

12 clean areas, but they were not.

13 That, I think -- I mean, we have dealt 

14 with situations where we were able to show that 

15 there were adequate incident records. I can't 

16 remember where we did that, a site where we did 

17 a detailed investigation. But at Savannah 

18 River, it's been a little bit more difficult 

19 because --

20 DR. NETON: I think you're talking 
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1 about something slightly different. I'm 

2 talking about if there are incident samples in 

3 your original log books, they better show up 

4 there in the summary data.

5 DR. MAKHIJANI: Oh, I see.

6 DR. NETON: Whether they had 

7 properly quantified or evaluated incidents, I 

8 think is another issue we're going to talk about, 

9 probably in this next item.

10 DR. MAKHIJANI: Okay, fine.

11 CHAIRMAN MELIUS: Can I just 

12 interrupt a second, just thinking procedurally. 

13 And it actually goes back to Jim's comment.

14 In terms of going forward, maybe one 

15 way of thinking about this is that we set -- I'll 

16 let you to think about this and maybe we'll come 

17 back and talk about it more later.

18 But we sort of set a time limit and say, within 

19 the next two or three weeks or something like 

20 that, is that we give comments to Jim.
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1 (Laughter.)

2 DR. NETON: I was going to say, there 

3 goes my vacation.

4 CHAIRMAN MELIUS: You’ve got a 

5 reprieve, you can relax on the beach and be fine. 

6 And if SC&A has something more they want to 

7 elaborate on that wasn't in your report, they 

8 have more time. And then we get that to Jim.

9 And then we do a revision after that. 

10 Because that I think deals with some of the 

11 capture, it also gives, you know, thoughts you 

12 have on the plane on the way back, I wish I had 

13 said whatever, brought up this or that and 

14 re-look at it. And, again, not that this won't 

15 be reviewed again or whatever. But I think that 

16 may be helpful.

17 And I also want to have everybody 

18 sort of scribbling notes, if there are things 

19 that they think of now, to write them down now 

20 and so we can get back to Jim. Maybe it’s a 
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1 simple email, maybe it's something in an edited 

2 the document, but whatever works best for you.

3 DR. NETON: That'd be great.

4 CHAIRMAN MELIUS: And now that we 

5 know when Jim's vacation is, shall we say three 

6 weeks? Or what was your --

7 DR. NETON: Well, I think, you know, 

8 the next few weeks are going to be difficult for 

9 me because I've got some vacation scheduled.

10 Three weeks is fine. If you get me 

11 these things in three weeks, I can digest them 

12 and try to incorporate them, you know, give you 

13 some time to do that. And that's what I'm 

14 looking for, some valuable feedback.

15 CHAIRMAN MELIUS: John, Bob, Arjun?

16 DR. MAKHIJANI: Bob, does that mean 

17 -- we’ve sent you something.

18 (Simultaneous speaking.)

19 CHAIRMAN MELIUS: I’m just saying, 

20 something beyond --
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1 MR. BARTON: As a result of this 

2 meeting.

3 CHAIRMAN MELIUS: Yes. As a result 

4 of this meeting, you have additional --

5 DR. MAKHIJANI: So basically 

6 elaborating on, you know --

7 DR. NETON: Anything that’s either 

8 changed or is added based on you feedback. 

9 Because you did provide some feedback. 

10 MEMBER BEACH: For me, not to move 

11 that part too far, the minimum 30 samples, I'd 

12 be interested to hear a little bit more about 

13 that maybe. Because we didn't really -- I think 

14 you expected more comments on it.

15 DR. NETON: Well, only because we 

16 got comments the last time we talked about this.

17 MEMBER BEACH: So I’d want to make 

18 sure we --

19 DR. NETON: You know, what’s the 

20 statistical basis for it, why is it valid. And 
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1 there really is no real --

2 MR. STIVER: To me, it’d be kind of 

3 retreading a little bit of this. I know in 

4 RPRT-53 we talked quite a bit about the minimum 

5 numbers of samples required from the statistical 

6 standpoint.

7 MEMBER BEACH: I know we had but --

8 MR. STIVER: And there's always 

9 going to be some objectivity or subjectivity 

10 involved in looking at what whatever group you 

11 have, whether it’s truly adequate for this 

12 particular group.

13 DR. NETON: The bottom line is 

14 there's no really good, hard and fast 

15 statistical analysis that one can do to say that 

16 30 is appropriate.

17 DR. MAKHIJANI: That's true.

18 DR. NETON: Because there's a lot of 

19 things pointing to that that maybe it's okay, but 

20 that you really can't justify it based on a 
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1 purely statistical -- especially when you're 

2 doing something like this where you have, you 

3 know, the 50 year old data and, you know, what 

4 are the exposure potentials to begin with, and 

5 who was monitored and how many people were 

6 exposed? I mean, it's a lot of different things 

7 come into it.

8 MR. STIVER: It’s not like a 

9 traditional approach where you can go out there 

10 and decide how much more data you need to 

11 collect. And you only have so much to begin with 

12 --

13 MEMBER BEACH: Yes, sure. I 

14 understand that. I just --

15 CHAIRMAN MELIUS: I think we had a 

16 good discussion on that at that in-person 

17 meeting in Cincinnati.

18 DR. MAKHIJANI: What you're saying 

19 is that 30 may not be enough. But certainly you 

20 need at least 30. It could be more than that.
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1 DR. NETON: Oh, yeah. Thirty is 

2 minimum.

3 DR. MAKHIJANI: Harry, am I 

4 remembering right, in a few of the examples that 

5 we did, that we showed that 30 was not enough in 

6 some cases and 30 was enough in other cases?

7 DR. CHMELYNSKI: Yeah, that's 

8 right, Arjun. When the GSDs sort of came up 

9 around four or five or higher even 30 may not be 

10 worth looking at.

11 DR. NETON: Right. Yeah, we 

12 agree, it's on a case-by-case basis. I just 

13 wanted to put something down here to say, you 

14 know, I don't want someone going through doing 

15 a coworker model and they have 15 samples per 

16 year and say, oh, here’s your coworker model. No, 

17 let's talk about is that reasonable?

18 CHAIRMAN MELIUS: And what we 

19 eventually put in, let's also be aware that there 

20 are other situations where 30 is not adequate 
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1 either.

2 DR. NETON: There are a lot of 

3 caveats.

4 CHAIRMAN MELIUS: Yeah.

5 DR. MAKHIJANI: It would be helpful, 

6 Jim, if there were a couple of examples in this 

7 document that didn't kind of say, okay, here's 

8 a number or here's a bright line but --

9 DR. NETON: Examples of this --

10 DR. MAKHIJANI: Like this 

11 particular issue, or maybe in other cases also. 

12 But in this particular case, we actually have 

13 some work that was done that showed the kind of 

14 consideration that went into figuring out --

15 DR. NETON: I hadn't thought about 

16 putting examples in, but it’s worth considering.

17 CHAIRMAN MELIUS: Well, we have a --

18 speaking of examples, I think there's -- and I 

19 haven't seen any of this, so I don't know, but 

20 there's an SRS review that is underway right now 
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1 that we were hoping to be ready for this meeting. 

2 It wasn't, and, you know, the SRS Group has 

3 referred to us to review. Very nice of them. 

4 (Laughter.)

5 CHAIRMAN MELIUS: They didn't tell 

6 us, but that's okay. So maybe that will be one 

7 we can think about as going through and think 

8 about how this would apply there and so forth.

9 Now, I don't think we're expecting 

10 all the documentation to be in the original 

11 report and so forth. But it would address all 

12 of what we might have talked about here or will 

13 talk about.

14 But it will be a way of going through 

15 and sort of seeing are we missing something or 

16 are we not being appropriate or whatever.

17 DR. MAKHIJANI: Jim, are you 

18 thinking of the RPRT-55 review, the trivalent 

19 actinides?

20 DR. NETON: No. That was done, I 
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1 thought.

2 DR. MAKHIJANI: No, the thorium one 

3 was --

4 MR. BARTON: There was some overlap 

5 because they use the same data.

6 DR. NETON: Yeah, that’s what I was 

7 thinking.

8 MR. BARTON: As far as examples go, 

9 I think we can actually sort of generalize it a 

10 little bit. Your example of where, you know, 

11 maybe it's okay to have less than 30, the example 

12 of where you only have a handful of workers who 

13 are actually handling material.

14 In that case, you're not going to get 

15 more than 30, because there just simply weren't 

16 that many workers there. That's an instance 

17 where 30, you'll actually have to get to that, 

18 I guess, what we call a threshold.

19 And then, as Harry just mentioned, 

20 there are situations where just analyzing the 
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1 data, the geometric standard deviation or 

2 variance are such that 30 really becomes no 

3 longer sufficient as that sort of baseline 

4 number.

5 But I understand why 30's in here, 

6 because you have to start somewhere. But I 

7 think those are the two generic examples where 

8 having less than 30 is okay. And there might be 

9 situations where having 30 is just not 

10 sufficient.

11 DR. NETON: Yeah, I agree with that.

12 MEMBER ZIEMER: This is Ziemer. Can 

13 I add a comment?

14 CHAIRMAN MELIUS: Yes, certainly. 

15 Anytime, Paul.

16 MEMBER ZIEMER: Thanks. And I agree 

17 with Bob. I think what happens, and 30 is a good 

18 example of this, it’s where the burden of proof 

19 changes. If you’re below 30, someone has got to 

20 make a case for why that's okay.
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1 Once you pass that point, someone has 

2 to make a case for why that isn’t okay. It 

3 depend on who's crossed that line, in a sense. 

4 But I think that that's what the previous speaker 

5 was -- I didn’t know who was saying that, but 

6 that's basically it. It changes the burden of 

7 proof, is what happens when you cross the bright 

8 line.

9 CHAIRMAN MELIUS: Now that I've got 

10 my dig in at SC&A about their report wasn't done, 

11 I'll give another example we might be looking at 

12 is INL, where the internal exposure coworker 

13 model seems to be in limbo as of your updated 

14 report for this meeting.

15 DR. NETON: Yes. Well, that --

16 CHAIRMAN MELIUS: And, again, I'm 

17 just saying it's something we can -- it might be 

18 something we want to look at as an example of 

19 these kinds of issues and so forth that would be 

20 -- if the timing is appropriate.
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1 DR. NETON: No, I agree.

2 CHAIRMAN MELIUS: Yeah.

3 DR. NETON: Part of the issue with 

4 the INL, of course, is that we're sort of waiting 

5 on a resolution --

6 (Simultaneous speaking.)

7 DR. NETON: I get routinely asked, 

8 well, how should I do the analysis? And I say, 

9 well, maybe after Monday I'll let you know. 

10 Maybe that's not going to happen.

11 CHAIRMAN MELIUS: Since everyone's 

12 to blame, we can bring it all back and everyone 

13 can share with -- or if there's parts of that that 

14 are in process that may be good to talk about, 

15 that would be helpful.

16 Again, I don't know the details. 

17 Again, it's as much to assure us that we're not 

18 missing something that you or your, you know, 

19 staff or ORAU staff are finding problematic or 

20 whatever.
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1 DR. NETON: That's a good point.

2 MR. BARTON: Well, with regards to 

3 the SC&A report that is currently late, really 

4 there were some findings that were related to 

5 that and also the thorium, since they pretty much 

6 use the same database. And one of the findings 

7 we had in both reports, essentially, was there 

8 were periods where years were grouped together.

9 And it appeared that maybe one of 

10 these in there, and I think there was text to that 

11 effect, was that, you know, we wanted to reach 

12 a certain threshold so that we could do some 

13 comparative studies.

14 And, you know, so we came back and 

15 said, well, there's that and, you know, that 

16 sounds okay, but you really have to do that 

17 analysis of when you start pushing these 

18 different periods together. Are we actually 

19 comparing a period of different exposure 

20 potential, but we're building together an 
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1 averaging them and that information's being 

2 lost?

3 And, Jim, I don't want to steal your 

4 thunder, because you do describe how you should 

5 be able to go in and look at the operational data 

6 and production data, air sampling, even some, 

7 you know, claimant interviews and such, where 

8 they say, you know, this really sort of nasty 

9 campaign started at such and such time.

10 And then that's maybe when you want 

11 to go back and look specifically at some periods 

12 of time to see if it's, one, appropriate to 

13 combine multiple time intervals.

14 So I guess that's what I'd say as far 

15 as the trivalent, and thorium issue and this 

16 notion of is 30 workers going to be enough.

17 And I think that the case has to be 

18 made when it's not, and that perhaps you have to 

19 combine certain years. The case has to be made 

20 that conditions were sufficiently similar to 
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1 allow that combination.

2 DR. NETON: I have no disagreement 

3 with that. I mean --

4 CHAIRMAN MELIUS: And again, I think 

5 that is a good example and would be, I could see, 

6 put into this document as for example.

7 DR. NETON: Yes.

8 CHAIRMAN MELIUS: That and just a 

9 sentence or two that would be helpful and I think 

10 would not be an uncommon situation.

11 DR. NETON: Agreed. Okay, I think 

12 we've covered data adequacy 2.1. Now we can get 

13 into some more -- less controversial issues, 

14 just kidding.

15 (Laughter.)

16 DR. NETON: The second, 2.2, deals 

17 with the application --

18 (Simultaneous speaking.)

19 DR. NETON: But, yes, the first one 

20 was thought, okay, can you build a model. And, 
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1 you know, what do you need to have to move 

2 forward. And now that you have a model, how do 

3 you do it?

4 And, you know, in here is some very 

5 familiar language that you've heard probably 

6 more than you care to about how we feel about who 

7 was monitored.

8 We've outlined the three types, we 

9 believe, of monitoring programs, that they were 

10 either routine which was a representative 

11 sampling of the workers, or there were routine 

12 measurements of workers with the highest 

13 exposure potential or there were collections of 

14 samples after identification of the incident.

15 Of course, the incident samples 

16 could permeate both one and two. Because you're 

17 always going to have incidents in the midst of 

18 a routine sampling program. We recognize that.

19 The one that's not on here that 

20 oftentimes, you know, I sort of get the feeling 
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1 that people are implying is that the workers with 

2 the least potential, or the lowest potential for 

3 exposure, were monitored.

4 And I've never really believed that 

5 to be true. You know, I know the argument about 

6 the NTS workers is raised as sort of 

7 representative of that. But that was added as 

8 an SEC because it was primarily an 

9 incident-driven sampling program.

10 So, you know, that's the case where 

11 it would fall into Category Number 3. And we can 

12 talk a little bit more about what our thoughts 

13 are on incident sampling programs. But I think 

14 that was, correct me, was misidentified as a 

15 Category Number 2.

16 DR. MAKHIJANI: I think that was --

17 (Simultaneous speaking.)

18 DR. NETON: I think that was the 

19 issue that I, as I recall, but anyway, it wasn't 

20 considered.
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1 DR. MAKHIJANI: I just want to set the 

2 record straight a little bit. I don't believe we 

3 ever said that workers with the lowest exposure 

4 potential were monitored. And I don't believe, 

5 it's a long time ago, but I don't believe we ever 

6 said that at Nevada Test Site.

7 I think what happened there, and 

8 maybe it would be exemplary for what we're trying 

9 to untangle here, is there was a claim that the 

10 most exposed workers were monitored. And 

11 here's 100 of them, and we know what the external 

12 and internal were. So there's a kind of 

13 procedure that was developed.

14 And leaving aside that 

15 internal/external turned out to be not very well 

16 correlated, or at least a correlation couldn’t 

17 be established went to the question, well, you 

18 know, were the most exposed people monitored or 

19 were the monitored people representative of the 

20 most exposed, even if some sub would do that.
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1 And I think what happened is that we 

2 were able to show that some of the people who 

3 were, some groups of workers had potential for 

4 higher exposure based on some of their 

5 monitoring data, which was pretty sparse. Then 

6 the groups were mostly monitored.

7 So we weren't saying, I don't think, 

8 and I don't think we've ever said that at any 

9 site, although I have not been involved in many 

10 of the sites that the lowest exposed workers were 

11 monitored, I think what we've often challenged 

12 is the assertion that the most exposed workers 

13 were monitored or that the monitored workers 

14 were representative of the highest exposure.

15 DR. NETON: Well, so here --

16 DR. MAKHIJANI: That's a very 

17 difficult thing to prove. And it's been sort of 

18 a point of debate.

19 DR. NETON: Well, here you have, and 

20 Dr. Melius just pointed it out, then you get in 
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1 a situation is it a Category 2 monitored 

2 workforce or Category 3?

3 Category 2 is workers with the 

4 highest potential. Category 3 is an 

5 incident-based sampling program. It turns out 

6 Savannah River, I mean, NTS was a combination.

7 You know, the rad techs seemed to be 

8 on a routine monitoring program, because they 

9 were all over the place. They wanted to know 

10 what they were exposed to so they monitored them 

11 fairly frequently.

12 The workers, and I have to say, NTS 

13 is a somewhat different beast because of the way 

14 things were run there, but the workers were 

15 incident-based, based on the shots and, you 

16 know, did they feel?

17 So there you have two groups of 

18 workers that I think were under two different 

19 monitoring programs. And I think I've seen that 

20 correctly, you know, identified that issue. We 
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1 eventually agreed that, you know, we had it 

2 miscategorized.

3 That doesn't invalidate anything 

4 we're saying here though. What I'm saying here 

5 is true, that they're going to fall into one of 

6 these three categories. And sometimes you're 

7 going to have a mixed bag which, I guess, is what 

8 I would agree happened in that Nevada Test Site.

9 CHAIRMAN MELIUS: And I think the 

10 issue of is there a fourth one, only lowest 

11 exposure, I think, you've got covered on one. 

12 If it's representative sampling, it should be 

13 representative of high and low. I mean, that's 

14 --

15 MEMBER BEACH: Well, doesn't this 

16 kind of go back to the way they set their programs 

17 up too, I mean, individual sites?

18 CHAIRMAN MELIUS: Yes. And it 

19 changes over time.

20 MEMBER BEACH: It does change.
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1 CHAIRMAN MELIUS: And it changes 

2 with new processes being brought in and it's 

3 new directives from above, et cetera, or hiring 

4 more health physicists and that kind of thing --

5 DR. NETON: It's fairly easy for 

6 sites that handle uranium, one radionuclide, a 

7 lot of monitoring, easy to measure. Sites that, 

8 as we know, handle multiple radionuclides that, 

9 the national laboratories are great examples of 

10 that, are very hard to convince yourself that 

11 they had any routine type monitoring program.

12 The question then is did they really 

13 need it? Then we have to get into that analysis. 

14 Because, just because they didn't have it 

15 doesn't mean it wasn't needed.

16 And, you know, we're going to talk a 

17 little about that. You know, what kind of other 

18 health physics indicators are there that can 

19 demonstrate and make one feel comfortable that 

20 the workers really weren't exposed.
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1 Because I've always maintained, and 

2 I think it's not just me, but you don't use people 

3 as human air samplers. You set up your program 

4 so that they aren't exposed.

5 The routine program is really just 

6 there to convince yourselves that yeah, we did 

7 a good job making sure they weren't exposed or 

8 the exposures were kept as low as we thought they 

9 were, based on our administrative and 

10 engineering controls.

11 So, you know, these are not about 

12 using people as air samplers. We've got to keep 

13 that in mind. Because there are other things in 

14 place, other barriers.

15 CHAIRMAN MELIUS: But a control 

16 program is not, by itself, adequate for doing 

17 dose reconstruction. And I think, you know, and 

18 this sort of permeates the whole dose, all 

19 EEOICPA, is the sense that, you know, record 

20 keeping wasn't done in a way, and monitoring 
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1 wasn't done in a way necessarily that will allow 

2 you to go back and do dose reconstruction.

3 And to me that's how I've interpreted 

4 the national labs, because we just have so little 

5 data there that it's just not feasible to turn 

6 that data into a plausible model. Because 

7 diversity of exposures combined with the lack of 

8 monitoring data.

9 It does not mean they weren’t 

10 necessarily protected. I agree that I think 

11 that applies to a lot the sites where we've done 

12 SECs, not just the national labs, but, you know.

13 I mean, look how many sites we've 

14 done because the personnel wasn't categorized 

15 and kept track of in a way that would lend itself 

16 to dose reconstruction.

17 And that's people who -- now, that's 

18 not necessarily a problem, I mean, we don't know, 

19 you know, we talked about the example earlier. 

20 So I think that's something that's sort of 
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1 separate from this.

2 I didn't think, to me the key thing 

3 on the type of sampling program, where we're 

4 having a problem with coworker models, is can you 

5 do a coworker model where you have one group 

6 that's under one kind of regimen and another 

7 under another type of sampling program.

8 And to me that's very, very difficult 

9 to do in a statistically adequate way. And I 

10 think that's what we're, you know, what we need 

11 to wrestle with.

12 DR. NETON: I think that's worth 

13 pursuing. The first, I'd just like to talk 

14 about one of your first comments about control 

15 programs not being sufficient for dose 

16 reconstruction.

17 I think that may be true in the older 

18 eras, you know, 60s, 50s, those where, you know, 

19 we really don't have a good program.

20 But in my opinion, in the modern era 
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1 where you have 10 CFR 835 where, you know, 

2 there's a requirement that people with less than 

3 100, people who have more than 100 millirem 

4 potential for internal exposure need to be on a 

5 monitoring program.

6 That's pretty well evaluated, and 

7 the documentation is there and the controls, I 

8 think, are there and the demonstrations. I 

9 think, in those situations, you can.

10 But I would agree in the earlier 

11 days, and obviously NIOSH has agreed with that, 

12 many of the national laboratories just can't 

13 make the case.

14 CHAIRMAN MELIUS: Yes. And it's 

15 diversity of operations --

16 DR. NETON: Yes, right.

17 CHAIRMAN MELIUS: -- et cetera. 

18 But production facilities and situations are 

19 different.

20 DR. NETON: So, you know, in this 
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1 Section 2.2, we're talking about the routine 

2 monitoring program and how one should go about 

3 determining if it was actually a good routine 

4 monitoring program that could be used for a 

5 coworker model.

6 And I've highlighted here what must 

7 be evaluated to demonstrate that it was a good 

8 program with a representative of the exposed 

9 population, the workers with the highest 

10 exposure potential -- oh, sorry, I got ahead of 

11 myself here a little bit.

12 MR. BARTON: I think that was really 

13 SC&A’s main comment about this section was that 

14 we agree, for a coworker model to be valid, we 

15 sort of have to fit it into one these two --

16 DR. NETON: Right.

17 MR. BARTON: -- categories. And we 

18 just want to make sure, maybe just a little word 

19 tweaking would take care of it, that it was never 

20 assumed, a priori, that that's the case. But 
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1 you have to sort of demonstrate it somehow.

2 DR. NETON: Yes, yes. I agree. 

3 And, you know, we've gone, lately it's been more 

4 of a standard mode of operation to go back and 

5 look at the procedures, and at Savannah River in 

6 particular, and say here're the sheets, the 

7 checklists that say who has to be on a monitoring 

8 program.

9 And then not only is that sufficient 

10 in itself, but you've got to go back and say did 

11 they really take those samples.

12 MR. BARTON: Yes, did they do it?

13 DR. NETON: And once you can make 

14 that case, then you're pretty far along saying 

15 I think we've got a fairly good situation.

16 All right. This is one of these 

17 paragraphs though I think that maybe some 

18 checklist-type items for filling out, it would 

19 benefit that.

20 Again, I like to keep the 
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1 implementation guide simple, mostly because I 

2 don't like to write 60-page documents. That's 

3 fine. No, I think you need to have an upper tier 

4 that is more general.

5 CHAIRMAN MELIUS: That's why you 

6 have staff and ORAU.

7 DR. NETON: You can proclaim what's 

8 important and then have the details fleshed out.

9 CHAIRMAN MELIUS: Yes, that's 

10 right.

11 (Laughter.)

12 DR. NETON: Okay. Now, this last 

13 one's going to be, I'm sure, subject to some 

14 discussion which is incident-based sampling. 

15 Can you use an incident-sampling program to do 

16 anything as far as coworkers?

17 MEMBER ROESSLER: Could we go back 

18 to Paragraph 2 there?

19 DR. NETON: Sure.

20 MEMBER ROESSLER: I think you have a 
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1 correction to make in the last sentence. Don't 

2 you have that backwards?

3 MR. BARTON: Yes, it does look like 

4 it is in backwards, but --

5 (Simultaneous speaking.)

6 DR. NETON: Representative samples 

7 or worker -- in these cases the assignment of 

8 coworker dose --

9 (Simultaneous speaking.)

10 DR. NETON: -- for distribution 

11 measured values --

12 (Simultaneous speaking.)

13 DR. NETON: Oh, yes.

14 MR. BARTON: Or representative, see 

15 that?

16 DR. NETON: Or representative, yes.

17 MEMBER ROESSLER: Okay. Just 

18 wanted to --

19 DR. NETON: It is backwards.

20 MEMBER ROESSLER: -- point it out to 
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1 you, okay.

2 DR. NETON: It must have been right 

3 around lunch time when I wrote this.

4 (Laughter.)

5 DR. NETON: Okay. Thank you for 

6 that comment. Now, this last paragraph, I'm not 

7 willing to agree that incident sampling programs 

8 in themselves are not useful to develop an 

9 inference as to what the unmonitored coworkers' 

10 exposure was.

11 And this may, and I know Dr. Melius' 

12 opinion on this, because he just said it, but if 

13 you have a program in place that has put controls 

14 in there, I mean, I'm talking about situations 

15 where there's glove boxes, there's alpha CAMs, 

16 there're smears taken daily and there's no 

17 evidence of upset conditions.

18 And then all of a sudden, so the 

19 workers aren't monitored for good reason, all of 

20 a sudden there's a clear indication of an 
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1 incident out there. Someone came out of an area 

2 where they got contaminated, the alpha CAM went, 

3 you know, that sort of thing. Then you have an 

4 incident sample.

5 So if you have a couple of incident 

6 samples on a worker, and you assume that those 

7 incident samples -- now this is where it is a bit 

8 of a stretch -- are representative of a chronic 

9 model, which is what our coworker models are, 

10 then you've bounded the exposure of those 

11 workers.

12 It can't be any higher than that for 

13 that worker. His chronic exposure could not be 

14 higher than the chronic exposure that put 

15 through the value of that first incident sample 

16 that may have happened three or four months into 

17 the monitoring period. So I think you can get 

18 some useful bounding information out of that.

19 DR. MAKHIJANI: Is that a way to 

20 bound something though?
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1 DR. NETON: I mean, it is.

2 DR. MAKHIJANI: Because that 

3 incident, because the way you described your 

4 routine monitoring was a worker protection 

5 program, right?

6 DR. NETON: It was --

7 (Simultaneous speaking.)

8 DR. MAKHIJANI: -- equipment to make 

9 sure workers are not exposed.

10 DR. NETON: Right.

11 DR. MAKHIJANI: And then you do your 

12 monitoring to verify that your systems are 

13 working.

14 DR. NETON: Right.

15 DR. MAKHIJANI: So that's a 

16 situation in which you're not expecting to find 

17 anything.

18 (Simultaneous speaking.)

19 DR. MAKHIJANI: You actually have, 

20 well, most of the time you have to have results 
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1 --

2 DR. NETON: In the early years, we 

3 were allowed, it was not so --

4 DR. MAKHIJANI: That's true. In 

5 the early years they were allowed to be exposed 

6 quite a lot.

7 DR. NETON: Yes.

8 DR. MAKHIJANI: But I don't see how 

9 you can, because incidents, by their very 

10 nature, are not representative of anything 

11 normal and you have very high results.

12 And then you're necessarily going to 

13 get into situations where there, well, basically 

14 you're saying this kind of incident is occurring 

15 all the time which --

16 DR. NETON: No, no. I would say as 

17 a, a routine monitoring program, in my mind, 

18 would be in place when there was, there was a 

19 reasonable potential to generate airborne 

20 radioactive materials, for somewhere that has 
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1 processes going on that are either, you know, 

2 pushing things through an extrusion press and, 

3 you know, you could have a pop, or something like 

4 that or grinding on uranium surfaces.

5 That's when you have routine 

6 monitoring. But even though you've got 

7 engineering controls in the early days, people 

8 were, it was acceptable to have routine airborne 

9 in the area. We were just trying to make sure 

10 that it didn't exceed what you were expecting 

11 based on your controls.

12 But there are situations in Savannah 

13 River, I keep harping on that or hate to keep 

14 harping on that, is there are situations where 

15 their people are, a very confined process, 

16 extremely confined, you have no expectation that 

17 there's going to be anything.

18 But you have enough workplace 

19 indicators there to let you know when something 

20 does go off the B- not a normal situation.
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1 And then, in that situation, you have 

2 a bioassay sample that can be used to bound the 

3 workers' exposure through the first six, eight 

4 months of the monitoring period. And he's no 

5 higher than that.

6 Because that incident sample is not 

7 only representative of his exposure during the 

8 incident, it's representative of his exposure 

9 during the first few months of the year, or 

10 whatever time period elapsed.

11 DR. MAKHIJANI: That second part, 

12 the representative --

13 DR. NETON: I mean it's bounding. 

14 It's plausibly bounding.

15 (Simultaneous speaking.)

16 DR. NETON: I'm open for discussion. 

17 That's the way I view it right now.

18 DR. MAKHIJANI: Yes, yes.

19 CHAIRMAN MELIUS: I think the 

20 threshold's going to be pretty high on that. 
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1 I'm not saying those situations don't exist, but 

2 I think it would be a situation where they come 

3 up with a plausible upper bound.

4 I can agree on the bounding would be 

5 an easier threshold to meet. But I think a 

6 plausible upper bound, I think, is going to be 

7 much harder, because it --

8 DR. NETON: Well, I think you'd be 

9 hard pressed to convince me that the unmonitored 

10 workers that were working in those same 

11 situations had a higher exposure than those guys 

12 who were incident samples.

13 CHAIRMAN MELIUS: But that’s 

14 bounding. What I'm saying is plausible is a 

15 representative of, you know, is that 

16 sufficiently accurate to represent those 

17 workers?

18 And what it’s going to do is it'll 

19 come down to what is the nature, the number of 

20 incidents, the documentation of the incidents, 
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1 the number of, you know, what levels were found 

2 and so forth.

3 And I think, I don't want to say it 

4 can't be done. But at the same time, I would be 

5 very skeptical of accepting it.

6 DR. NETON: I don't disagree that 

7 it’s a high bar to prove. I mean, I think I've 

8 sort of said that in the last seconds here. You 

9 can demonstrate the effectiveness in the 

10 engineering controls, adequate to prevent 

11 exposure except during upset conditions. It 

12 may be possible to use incident-based.

13 CHAIRMAN MELIUS: I hate to close 

14 the door on it just because --

15 DR. MAKHIJANI: Actually, I am going 

16 to keep the door partially open, give you two 

17 examples that are completely different. 

18 They're different.

19 So if you have a situation like Ames 

20 where you know that you are having a lot of these 
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1 blow outs and that they happen fairly 

2 frequently, and we, of course, discuss this at 

3 some length around the 250 day issue.

4 You can make a case there that there 

5 were so many of these incidents that, if you 

6 could characterize the number, then you could 

7 say that this was some, almost a kind of a routine 

8 exposure, although you weren't looking for blow 

9 outs, they happen very often.

10 On the other hand, there was that 

11 incident with the pig at Savannah River, where 

12 there was a cobalt something, I can't remember 

13 exactly. It was, you know, it was clearly a very 

14 unique thing that happened once.

15 You can't take something like that 

16 and say I'm going to take this incident, I think 

17 it might even be an external, but I'm not quite 

18 sure.

19 But if you have something that's 

20 clearly unique, a red oil explosion, you know, 
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1 very rare, and say that that's going to give you 

2 some plausible upper bound. It'll give you a 

3 big number, but it won't be plausible. So I 

4 think you have some burden of proof about the 

5 plausibility of the condition.

6 DR. NETON: Yes.

7 DR. MAKHIJANI: Without saying that, 

8 you know, you can never do this. I don't think 

9 you can do it often.

10 DR. NETON: I don't disagree. It's 

11 a high bar to demonstrate. But --

12 CHAIRMAN MELIUS: I mean, if you 

13 want to try it, and you can get Stu to take it 

14 on --

15 DR. NETON: Well --

16 (Laughter.)

17 DR. NETON: See, in my opinion the 

18 reason that people weren't on a routine 

19 monitoring program is because they were pretty 

20 darn confident that there was a very, very low 
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1 potential for exposure. And if you can show 

2 that, you know, that it wasn't just negligence 

3 on their part, well I'm only going to --

4 CHAIRMAN MELIUS: Yes.

5 DR. NETON: The guys could have been 

6 exposed to quite a bit of material, but I'm only 

7 going to sample when I know that there was a 

8 problem.

9 I think, and most often, if you have 

10 a routine program in place, then you have an 

11 incident program in the same facility, there's 

12 a reason for that. And the reason is that it has 

13 been evaluated to be a very low potential 

14 situation. I mean, so you have to give some 

15 credit for that.

16 DR. MAKHIJANI: Why can't we find 

17 those Evaluation Reports? If we could find 

18 those, the job would --

19 DR. NETON: I think we have some at 

20 Savannah River. I think we do. I think there 
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1 are some. Yes.

2 MR. BARTON: Yes. I think that 

3 really feeds into the high bar of being able to 

4 establish a concept like this. Do the records 

5 we have actually cover the incidents that were 

6 at the site?

7 And was the monitoring that took 

8 place after those incidents adequate so that we 

9 are reasonably certain you captured all of these 

10 sort of acute intake scenarios?

11 And that would have to be part of sort 

12 of establishing also that the administrative 

13 controls would have detected any sort of off 

14 normal occurrence, and it was properly 

15 documented, and we have access to those 

16 documents.

17 CHAIRMAN MELIUS: In that case, why 

18 wouldn't we have some sort of a, you know, 

19 process-based whatever, coworker model-based 

20 ignoring the incidents and then adding the 
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1 incidents as individual exposures for those 

2 where it was documented? Because we're saying 

3 they’d be completely or close to completely 

4 documented and evaluated.

5 DR. NETON: Well, if the 

6 process-based analysis says there is no 

7 potential for exposure then I would agree.

8 CHAIRMAN MELIUS: Yes, yes, yes. 

9 There would be very little.

10 DR. NETON: I mean, that's what I'm 

11 saying. That's what I tried to say here. I 

12 said if you could demonstrate this thing is a 

13 locked tight situation, and I don't expect that 

14 there's any exposure here unless something 

15 really awry happened, and it would be easily 

16 noticeable, you know, and it wouldn't escape 

17 notice, because you're talking about plutonium, 

18 alpha CAM, you know, that kind of situation.

19 CHAIRMAN MELIUS: And I also think 

20 that we, you know, I think there's always going 
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1 to be individual situation and a variety of 

2 different approaches --

3 DR. NETON: Exactly.

4 CHAIRMAN MELIUS: -- that are going 

5 to be very specific to the site and the incident.

6 DR. NETON: Yes, exactly. And 

7 again, this is going to be more and more common 

8 as we approach the modern era, as we get past 10 

9 CFR 835 implementation. Hopefully, our 

10 documents out there, they can say here's what 

11 we've done to demonstrate that the potentials 

12 are very small, and we don't need to monitor 

13 these workers.

14 CHAIRMAN MELIUS: Well, can I 

15 suggest break now, since it's been two and a half 

16 hours. And 2:30 I should say, an hour and half.

17 MEMBER ROESSLER: It has been a one 

18 and a half, but that's okay.

19 CHAIRMAN MELIUS: Huh?

20 (Simultaneous speaking.)
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1 DR. NETON: It seems like two and a 

2 half hours.

3 (Laughter.)

4 CHAIRMAN MELIUS: According to my 

5 watch it’s been, yes, 46 seconds over an hour and 

6 a half.

7 MR. KATZ: Ten minutes, what do you 

8 want?

9 CHAIRMAN MELIUS: Ten minutes.

10 MR. KATZ: Ten minute break. Is 

11 everyone on the line? And I'm just going to put 

12 the phone on mute so you don't have to hear jibber 

13 jabber.

14 (Whereupon, the above-entitled 

15 matter went off the record at 2:33 p.m. and 

16 resumed at 2:51 p.m.)

17 MR. KATZ: Okay, we're back. Let me 

18 just check and see. Paul, do we have you on the 

19 line?

20 (No audible response.)
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1 MR. KATZ: Dr. Ziemer?

2 (No audible response.)

3 MR. KATZ: Maybe you’re on mute?

4 MEMBER ZIEMER: Yes, I'm on the 

5 line.

6 MR. KATZ: Oh, great. All right. 

7 I don't know if we need to check on anyone else? 

8 John, do you need to check on anyone, your 

9 people?

10 MR. STIVER: No, we don't need to 

11 check.

12 MR. KATZ: Okay.

13 MR. STIVER: They're expected to be 

14 ready.

15 DR. NETON: They're expected to be. 

16 If they're not, when we look for them --

17 DR. MAURO: Well, I'm here, of 

18 course. You think I'm going to leave you alone 

19 --

20 (Laughter.)
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1 CHAIRMAN MELIUS: Call the 

2 operator, cut off that line.

3 MR. KATZ: Okay. Let me just remind 

4 everyone that's on the line, Mr. Warren, if you 

5 could mute your phone, because we can all hear 

6 you. If you don't have a mute button, then you 

7 press *6. That'll mute your phone. Thank you.

8 CHAIRMAN MELIUS: Okay. Jim, go 

9 ahead.

10 DR. NETON: Okay. We're up to 

11 Section 2.3 which is titled Appropriateness of 

12 the Model Data for the Unmonitored Population.

13 In here we were just trying to get the 

14 point across that you need to look at the people 

15 that weren't monitored and make sure that it 

16 fits, you know, were the monitored workers and 

17 the unmonitored people really part and parcel of 

18 the same exposure group?

19 The idea here, of course, is you 

20 could have maintenance workers and such that are 
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1 doing very different tasks than the person who 

2 is running a lathe, you know, doing something 

3 else. So we need to be careful about that.

4 The converse of that is also true 

5 though if the unmonitored population had no 

6 potential for exposure then we don't really need 

7 to apply a coworker model at all. So I think 

8 it's important to look at both sides of the --

9 both sides of the fence.

10 Okay. And I think that's not too 

11 controversial. I'll move on to one section that 

12 is, Analysis of the Monitoring Data.

13 So, you know, this gets into the 

14 heart of the matter as to how we're going to 

15 construct the coworker model. You know, we've 

16 already, based on the first couple of sections, 

17 decided that the data reasonably represents the 

18 workers. And we want to figure out how to 

19 analyze the data.

20 And traditionally I have put down 
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1 what we've done. We can represent the data by 

2 a log normal distribution with a corresponding 

3 geometric mean and standard deviation to 

4 represent the distribution.

5 We talked about this last time, that 

6 workers who are considered to have been heavily 

7 exposed or potentially exposed, those who are 

8 working with materials where airborne 

9 radioactivity was possible, would receive the 

10 95th percentile of the exposure distribution.

11 Those that were not would receive the 

12 full distribution which would be the geometric 

13 mean and standard deviation.

14 I put a note in here, because I know 

15 we've been asked multiple times, or several 

16 times at least, well, how do you know? And 

17 actually it came up in SC&A's review.

18 It was like, well, it would be nice 

19 if you could define who those heavily exposed 

20 workers are. And it's been my feeling that you 
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1 really can't. You'd have to do it on, as I say 

2 here, also on a case-by-case basis.

3 Because there are some workers who 

4 could be classified as clerks, who you might 

5 think would not be heavily exposed, but they were 

6 involved in, you know, inventorying of materials 

7 and stuff in radiation areas that had a lot of 

8 high potential for airborne.

9 So I don't know if one can really say 

10 with any confidence, develop a list that is, of 

11 these workers. I mean, in my mind, people such 

12 as the trades workers, pipe fitters, those type 

13 folks, electricians, welders who worked in 

14 radiological areas would fall into that 

15 category.

16 The other side of the spectrum, I 

17 would say someone who had a job title as a 

18 secretary, possibly not, administrative folks 

19 who didn't really frequent the controlled areas, 

20 this came up in the GSI discussion.
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1 We'd have to be pretty certain, you 

2 know. I think the bar is pretty high to 

3 determine, you know, when it would be a 50th 

4 percentile versus a 95th percentile.

5 MR. STIVER: But, Jim, apparently 

6 it's kind of an ad hoc procedure that's left to 

7 the dose reconstructor to decide.

8 DR. NETON: Yes, yes. Pretty much 

9 so. I don't know if, you know, maybe we can 

10 develop some more general guidelines. But it's 

11 really difficult to say, you know, that these job 

12 categories are always going to be highly exposed 

13 and these aren’t.

14 But, you know, you could have 

15 pipefitters that never worked in a radiological 

16 area, electricians the same way. I mean, if you 

17 can clearly see that in the record, and it came 

18 out in the CATI and such, then, you know, it'd 

19 be silly. It'd be inappropriate to assign them 

20 the 95th percentile.
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1 MEMBER ROESSLER: If it's left to 

2 the dose reconstructor, and there're a lot of 

3 them, is there somebody then who overviews, 

4 well, they --

5 DR. NETON: Well, these are reviewed 

6 at several levels. I mean, you have the dose 

7 reconstructor, then there's the internal ORAU 

8 review, and then a NIOSH DCAS person, health 

9 physicist, that reviews every dose 

10 reconstruction before it goes out as well.

11 CHAIRMAN MELIUS: Yes. But this is 

12 at the Site Profile level we're talking about, 

13 not at the dose reconstruction level.

14 DR. NETON: No, no, this would be at 

15 the dose reconstruction level.

16 CHAIRMAN MELIUS: No, no, no, no. 

17 What I'm saying, we don't care about the dose 

18 reconstruction level here.

19 MEMBER BEACH: This is the 

20 overarching.
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1 CHAIRMAN MELIUS: We want the 

2 overarching. And I think that's one of the 

3 problems we're having, when we talk about this, 

4 is we tend to convolute the individual dose 

5 reconstruction which has a separate set of 

6 considerations. They overlap and they're, in 

7 some ways, sometimes very similar.

8 This sort of Site Profile coworker 

9 issue, which has frankly a lot more statistical 

10 issues to deal with, now the Site Profile issues 

11 essentially guide the, well, parts of it guide 

12 both.

13 DR. NETON: Right, yes.

14 CHAIRMAN MELIUS: And then the Site 

15 Profile issues and coworkers would obviously 

16 guide the individual dose reconstruction.

17 And one of the things that I just 

18 think about in terms of organizationally is that 

19 we sort of, in this section analysis, that we 

20 sort of back up and then include, you know, but 
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1 you're going to examine, evaluate the monitoring 

2 data that's available.

3 You're going to look for, you know, 

4 are coworker models going to be needed, are they 

5 necessary? But also, are there stratification 

6 issues that need to be dealt with?

7 Because I think those are, you know, 

8 would come about, and we've talked about this 

9 already, from other considerations other than, 

10 you know, trying to come up with a unifying 

11 model. Are there multiple models that need to 

12 be looked at?

13 DR. NETON: Right.

14 CHAIRMAN MELIUS: Which you already 

15 do, based on exposure. Again, Savannah River 

16 being an example.

17 DR. NETON: Well, that's covered in 

18 Section 4, I mean, I think.

19 CHAIRMAN MELIUS: Well, yes. But 

20 I'm just saying does some consideration need to 
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1 come back or you’re repeatedly going to be 

2 looking at the data. Then that's, well, 

3 shouldn't it be part of sort of initial decision 

4 making and consideration?

5 DR. NETON: Oh, I see. You're 

6 suggesting that we don't develop an all 

7 monitored workers model first, we actually start 

8 with pieces?

9 CHAIRMAN MELIUS: Well, no. You 

10 start with considering is an all monitored 

11 worker appropriate, going to be appropriate. 

12 Are there strata that are going to need to be 

13 considered? And then evaluate both, 

14 essentially. Because you really can't evaluate 

15 one without the other.

16 DR. NETON: No. Oh, I agree.

17 CHAIRMAN MELIUS: Because the 

18 reason you're not going to do a general model 

19 because there are strata that aren't 

20 appropriately captured through that.
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1 And I also think it addresses this, 

2 you know, 50th, 95th, that's going to be 

3 secondary to, you know, some of your decision 

4 making on what's the appropriate model for which 

5 groups and so forth. I'm just thinking more 

6 procedurally. If I were the --

7 DR. NETON: Yes.

8 CHAIRMAN MELIUS: -- the Site 

9 Profile author, I would start, I would array all 

10 this data, I would look at what monitoring data 

11 is available, obviously try to break it down into 

12 some meaty chunks and then parts of the facility 

13 or areas of exposure and then look at this.

14 Because I think we're coming to 

15 stratification sort of late. And I think if it 

16 was done earlier, I think it would sort of 

17 capture some of the other considerations better. 

18 I don't know how other’s feel on that --

19 DR. NETON: Yes.

20 CHAIRMAN MELIUS: -- this feeling 
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1 that, yes, because I think it, because it's not 

2 just a technical, statistical issue. It's an 

3 issue of sort of what's appropriate for that site 

4 and --

5 DR. NETON: Well, whether the data's 

6 stratified or not, this first paragraph is still 

7 valid. I mean --

8 CHAIRMAN MELIUS: No, no, I --

9 DR. NETON: -- distribution.

10 DR. MAKHIJANI: Well, I think what 

11 Jim is raising is, at that stage, you need to 

12 consider whether you're developing a 

13 distribution in a singular or whether you're 

14 developing in the plural.

15 So it seems if you decide, to develop 

16 a distribution you already made a lot of 

17 decisions underneath that.

18 DR. NETON: Well I tend to disagree. 

19 I think the development of the all coworker, all 

20 monitored worker distribution and the conscious 
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1 decisions we made to say you're going to get to 

2 95th percentile or four distributions depending 

3 on your job type.

4 Then one can go and see are there any 

5 distributions out there that would make that not 

6 appropriate, for reasons that we could talk 

7 about later but, you know, is it more 

8 claimant-favorable to assign the 95th 

9 percentile, recognize that we really can't put 

10 many people in these job categories very well. 

11 You know, we don't know.

12 Or is it okay? Just to say all B -

13 heavily exposed people get the 95th percentile. 

14 And, by default, the other ones get the 50th, I 

15 can't find any strata in there that give those 

16 people more dose than 95th percentile, because 

17 there's so much uncertainty.

18 And the other part of this issue is 

19 we could pretty much only do this kind of 

20 analysis at Savannah River. Let's be honest 
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1 about it.

2 What other facilities do we have the 

3 granularity of data to go and pull out all of 

4 these job categories and do these detailed 

5 analyses. In many cases, the coworker models 

6 are based on CEDR data. We don't know what these 

7 people did.

8 CHAIRMAN MELIUS: Then why are we 

9 doing them? That's --

10 DR. NETON: I'll bring in a table. 

11 Because I think that, as I showed, the 95th 

12 percentile is a fairly good claimant-favorable 

13 number to use when you look at it in terms of 

14 Probability of Causation analysis outcomes. 

15 There has to be a factor of two or greater almost 

16 increase in the median value for it to be more 

17 claimant-favorable to stratify.

18 CHAIRMAN MELIUS: But is it 

19 plausible?

20 DR. NETON: What do you mean, is it 
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1 plausible?

2 CHAIRMAN MELIUS: Sufficiently 

3 accurate, is it sufficiently accurate 

4 individual dose reconstruction being done?

5 DR. NETON: Well, again, these are 

6 the monitored workers. Now let's talk about is 

7 that bounding for the unmonitored workforce?

8 You're suggesting that if we don't 

9 know the job categories of all the workers that 

10 comprise the coworker model, then you can't do 

11 dose reconstructions for unmonitored workers. 

12 And essentially it becomes an SEC. In that 

13 situation then, the unmonitored workers are in 

14 the SEC, the monitored workers aren't.

15 CHAIRMAN MELIUS: Yes.

16 DR. NETON: And I find that to be, I 

17 don't know, disturbing's not the right word, but 

18 not appropriate, that people who have very low 

19 potential for exposure, or lower potential for 

20 exposure in general based on these models, based 
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1 on these monitored programs we talked about, 

2 most heavily exposed workers, representative 

3 workers, not incident-based, I find it hard to 

4 wrap my head around the fact that those people 

5 would be SEC. And the heavily exposed people 

6 that were monitored are not. That’s the end 

7 conclusion of that.

8 DR. MAKHIJANI: I think that's a 

9 little too schematic about what we’re talking 

10 about. So there may be like an administration 

11 building where people did not have, or almost 

12 never had contact with a radiological area.

13 And you could say that they were not 

14 monitored. They had lower exposure potential. 

15 And if you do any of these things, they're going 

16 to be covered. And I don't think you get any 

17 disagreement from me if you can show all those 

18 things.

19 What we are talking about though is 

20 workers who we know had presence and work in 
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1 controlled areas, many of whom were very 

2 infrequently monitored and where there was a lot 

3 of job diversity.

4 So that's really what we're talking 

5 about. I mean, in the interviews with the 

6 construction workers at Savannah River Site, 

7 Brad unfortunately isn't here.

8 But a number of construction workers 

9 described in considerable detail the variety of 

10 jobs that they did. And while they did their 

11 construction jobs, they also were kind of, you 

12 know, they were there and they were low on the 

13 totem pole and they did what they were told. And

14 mostly, a lot of that involved doing all 

15 different kinds of work.

16 So yet we find that their monitoring 

17 was incident-driven when, I think, if you look 

18 at it more objectively you can't conclude that 

19 they didn't have routine exposure potential or 

20 they weren't completely unlike production 
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1 workers in that respect.

2 We're not talking about a very 

3 schematic situation of low exposed workers and 

4 high exposed workers. We're talking about 

5 workers with exposure potential who had 

6 reasonably good monitoring and others who didn't 

7 have reasonably good monitoring. That's a 

8 tough situation.

9 DR. NETON: But there you're talking 

10 about a situation where you've got 

11 incident-driven people, bioassay, and it was 100 

12 percent incident-driven. And there is not, we 

13 don't have an ability to demonstrate the 

14 controls were in place to prevent exposures. I 

15 agree with you. That's NTS. That's exactly 

16 how that works.

17 CHAIRMAN MELIUS: Right.

18 DR. NETON: So I'm not disagreeing 

19 with you on that aspect.

20 CHAIRMAN MELIUS: Yes.
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1 DR. NETON: But I think, well, I 

2 don't know. I think, in general, we probably 

3 know the construction worker better, by job 

4 title. Because we know they are contractors, 

5 right. I mean, so that might be true. But I was 

6 speaking the other models --

7 PARTICIPANT: Could I say something?

8 (Off the record discussion.)

9 DR. NETON: Anyway, I don't know. 

10 If you really don't have definitive job 

11 categories for everybody in the coworker model, 

12 I don't know. I find it, I don't know.

13 I still feel the 95th percentile is 

14 the reason that we've adopted that and because 

15 there are multiple strata in there.

16 So if you go up to the 95th 

17 percentile, then you say, okay, we don't know. 

18 But we've bounded it and it's less than that. I 

19 guess your argument is it's not sufficiently 

20 accurate. But I don't know.
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1 CHAIRMAN MELIUS: Well, but I think 

2 the onus is on you to show that it's sufficiently 

3 accurate. And that's what we're trying to get 

4 at and trying to make sure that there's enough 

5 information presented to us the Advisory Board 

6 that we can evaluate that assessment.

7 And I think what's been happening is 

8 we're not getting that information. And so what 

9 we're trying to get at is a procedure to get that 

10 information.

11 So we're not trying to, we are being 

12 critical in the sense of trying to say let's get 

13 the information, and let's make sure that all 

14 approaches have been considered that are 

15 appropriate for a given site in a given 

16 circumstance.

17 DR. NETON: Yes. And I, okay, I 

18 agree. At Savannah River, I think, we can do 

19 this type of analysis. We're doing this type of 

20 analysis. But I guess this discussion started 
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1 when, you know, we said, well, you should 

2 stratify up front, not wait.

3 CHAIRMAN MELIUS: No, no, no. And 

4 it's not what I said. I said you need to 

5 consider stratification up front, that 

6 evaluating and analyzing data, you need to make 

7 that consideration earlier.

8 You know, but what I feel you're 

9 doing is jumping immediately to one model. And 

10 that becomes the null hypothesis. I'm not even 

11 sure it's, and then what you're telling us, well, 

12 that's a null hypothesis, it can't even be 

13 tested, that at every site other than Savannah 

14 River which is even more --

15 DR. MAKHIJANI: I mean, I think you 

16 alluded to this before but it did not kind of hit 

17 me over the head as it did today.

18 Okay, here's something that's very 

19 stark and big, I think you have to deal with. 

20 Because you cannot stratify in principle even to 
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1 -- then how do you demonstrate whether the 95th, 

2 you have to figure, I guess, we have ways in which 

3 we could demonstrate that if you had some job 

4 titles.

5 DR. NETON: Yes. There are some job 

6 titles, it's not zero. But I guess Savannah River 

7 is really robust with job titles. But it’s sort 

8 of the best one that I’ve seen. There are job 

9 titles at other facilities, but in some cases --

10 DR. MAURO: This is John. I'd like 

11 to, a thought struck me really early on. Could 

12 you give me a minute or so to try to communicate 

13 something that just hit me real hard about maybe 

14 you don’t need OPOS, and maybe you don't need 

15 pooled data either.

16 And maybe if you go back to doing it 

17 the right way, right, I'm sorry to use the word 

18 right way, but actually reconstruct the doses to 

19 real people the real way, the way, you know, you 

20 would like to do it all the time when you have 
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1 real, and you want to build a coworker model with 

2 that data.

3 Let me just try something out. What 

4 I just heard, Jim, is you can have a very large 

5 group of people working over some time frame. 

6 Let's say it's one year, and it's at a facility.

7 And you know that they were under a 

8 fairly robust health physics program, and that 

9 if there were any outliers, something unusual 

10 occurred that was being picked up and data would 

11 have been gathered unique for that person and you 

12 may have gotten a whole series of measurements, 

13 bioassay measurements.

14 So in theory, what you're really 

15 saying, you may have several hundred workers, 

16 maybe a thousand workers, working in a plant over 

17 a given year.

18 In a perfect world, you would 

19 reconstruct, you would say okay, we really have, 

20 within this population, we have the routine guys 
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1 that were monitored more or less routinely, 

2 maybe monthly, quarterly.

3 And then you have these smaller 

4 groups that were really monitored a lot, because 

5 they were involved in incidents. So you're 

6 watching everything, okay.

7 And then you have this other group of 

8 people that you don't really know, you don't know 

9 exactly what they did. And you want to 

10 reconstruct their doses using a coworker model. 

11 And you're suggesting OPOS.

12 Now, I heard you say that, well, you 

13 grab all these people. And rather than pooling 

14 the data for all these people, and I'll set aside 

15 these people that have data with high exposures 

16 because you caught those people. You have them. 

17 So let's put them over there on the side for a 

18 minute, in the parking lot.

19 And then you have all of these other 

20 people. But you don’t want to reconstruct 
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1 entirely the doses for 1,000 workers in that year 

2 to build this coworker model, this distribution. 

3 Because it's just implausible, it's just that 

4 the resources would be off the chart.

5 But you also said at the same time 

6 that that population of workers are not 

7 experiencing wild shifts in intakes. Because 

8 those would be unusual circumstances.

9 So if that's the case, and you're 

10 saying you have more or less a homogeneous group 

11 with individual variabilities from month to 

12 month that fall within the normal range of 

13 variability for this kind of operation or sets 

14 of operations, and there's nothing about it that 

15 could drive the special group of high exposures, 

16 why not sample randomly from that group, 30, 40, 

17 50, not all 2,000, and reconstruct their doses 

18 the right way.

19 And make that your coworker model for 

20 people you believe appropriately fall within 
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1 routine range of operations that occurred at 

2 that time period.

3 And that would be your coworker model 

4 for them, and you didn't have to go to OPOS. You 

5 know, why would you have to go to OPOS if you 

6 believe that your statistical sample, which 

7 would be select large enough as Harry discussed, 

8 maybe 30 is a good number, would represent the 

9 distribution for that group of people.

10 Then, I'm almost done, guys, bear 

11 with me. But then you have this other group of 

12 people who did experience something unusual. 

13 And there may be some group, by the way, I'm 

14 putting this nested, what's the word we're using 

15 for different like construction workers?

16 DR. NETON: Strata.

17 DR. MAURO: I'm putting that aside 

18 for a minute. Now we have, within that same 

19 group of people, there are some workers for some 

20 reason had higher exposures. That's got to be 
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1 a relatively limited number of people.

2 And the argument you're making is, 

3 well, you really don't need a coworker model for 

4 that, because they've been captured. You're 

5 not going to have any people like that.

6 But then you say to yourself but, you 

7 know, maybe we're wrong. Maybe there could have 

8 been a guy or some people that were involved in 

9 this unusual circumstance.

10 And I would say, again, if there's 

11 reason to believe that that might occur, why not 

12 reconstruct the exposures for the people 

13 involved in the unusual circumstance and have a 

14 coworker model for that set of circumstances 

15 using the right way of reconstructing the doses.

16 Then you really have two coworker 

17 models for this group of people. And it's not 

18 anybody involved in the strata. Notice I didn't 

19 say the word strata yet. I'm putting that off 

20 to the side.
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1 I'm just trying to think about the 

2 starting point which is a coworker model and 

3 trying to avoid having to go to OPOS if we can. 

4 That's a thought I wanted to leave on the table, 

5 it's a lot of this discussion.

6 DR. NETON: Okay. John, I'm not 

7 sure I quite followed exactly all of that.

8 CHAIRMAN MELIUS: And, John, since 

9 we're not talking about OPOS yet --

10 DR. MAURO: Okay.

11 CHAIRMAN MELIUS: -- I'm failing to 

12 see the relevance of this.

13 DR. MAURO: Okay. I thought that's 

14 where we were all heading.

15 CHAIRMAN MELIUS: No. At this 

16 point, we were really more talking about, we're 

17 still on the first paper.

18 DR. MAURO: Okay. My apologies.

19 DR. NETON: I guess to finish up what 

20 we were talking about, and when I talk about we 
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1 didn't have necessarily the job titles for all 

2 the people, at least as well at other sites that 

3 we had at Savannah River, I think it still goes 

4 back to Section 2.1 on data adequacy where it's 

5 incumbent upon us to evaluate the 

6 representativeness of the bioassay collection 

7 method and show that it's one of those two 

8 categories of workers.

9 Now, where is that, Section 2.2, I 

10 guess. Right. It must be established who was 

11 monitored and why they were monitored and 

12 whether they either representative of a sample 

13 or were they workers with the highest exposures.

14 And if we can do that. I think 

15 that's done. I mean, if you don't have, if the 

16 monitoring program captured all those workers 

17 then, you're okay with that coworker model, the 

18 thing that sticks out is the scenario that Arjun 

19 kind of likes to go back to, which is the incident 

20 stuff.
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1 You know, if you don't have a routine 

2 program, and it captured the full distribution 

3 of the workers then, yes, you have a set of 

4 workers that were just purely incident sampled. 

5 Then, yes, that's a different issue.

6 And we have added sites for programs 

7 that had incident-based sampling, only NTS, I 

8 think, Fernald recently had a Class added 

9 because the construction workers were 

10 incident-based.

11 CHAIRMAN MELIUS: But, I mean, I 

12 stratification is also appropriate for routine 

13 monitoring. It's the same, you know, in some 

14 ways a lot less complicated to evaluate. 

15 Certainly sample size, other issues that come 

16 up, which is why I'm just trying to --

17 DR. NETON: Right.

18 CHAIRMAN MELIUS: -- sort of move it 

19 up, just so that, again, we're thinking about 

20 doing this rather than making it as the final 
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1 step. That's all.

2 And you know, you can argue either 

3 way I am just trying to think, procedurally, as 

4 you're going through, it's sort of the checklist 

5 thing.

6 Are you considering it now? It may 

7 not be the first thing to evaluate, but in some 

8 cases it may be. Because it's distinct 

9 populations and --

10 DR. NETON: I completely understand 

11 what you're saying. I guess I'm worried about 

12 putting limits on these type of what ifs. Some 

13 are obvious. You could say, okay, chemical 

14 operators.

15 But how far down in the weeds do we 

16 have to get to demonstrate that, you know, there 

17 could be 30 different strata that have to be 

18 evaluated. And then you get into to diminishing 

19 sample sizes.

20 CHAIRMAN MELIUS: Yes. But -- we 
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1 were sort of maybe overextending ourselves in 

2 terms of, you know, the datasets are small at a 

3 lot of the sites.

4 But, you know, you can tell a lot by 

5 just sort of a line listing of, you know, 

6 breakdown by job title and what exposure data you 

7 have.

8 I mean, if it's going to tell you --

9 and even by your process information. I mean, 

10 the example you used, you have those 15 workers 

11 that were doing the special operation or 

12 whatever that are going to tell you what's 

13 appropriate for that.

14 And again, I'm not saying that this 

15 95th, 50th percentile approach is 

16 inappropriate, because it may be, it may, you 

17 know, be different ways to categorize that.

18 But I think it's going to, you know, 

19 how you prove it, I don't know. Because I think 

20 you're going to be basing it on looking at the 
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1 data and what makes sense in terms of that 

2 particular site, the operations, et cetera.

3 MR. BARTON: If I might, I think one 

4 place we were kind of, maybe got a little tripped 

5 up back there is the kind of example that we're 

6 talking about where you have real problems would 

7 be if you didn't have that job title information.

8 Because you may have a distribution, 

9 and you may believe that the relevant job types 

10 are captured there. But you also really can't 

11 tell the monitored workers in the upper tail of 

12 that distribution if there's a singular job 

13 title that would not be covered by an all worker 

14 model at the 95th percentile.

15 DR. NETON: Oh, yes. I mean, you 

16 have to have very good job worker information in 

17 order to do this. I mean, if you don't have it 

18 --

19 MR. BARTON: Right. And if you can 

20 establish job titles, I mean, there's a number 
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1 of things you can do. One example of what we did 

2 at Fernald for subcontractors where we said, 

3 okay, let's actually evaluate intakes for a very 

4 limited number of subcontractors that we had 

5 data for.

6 And we had information for when they 

7 were actually operating at the site. We did 

8 best estimates for that group of workers. 

9 And we said, all right, how is this all 

10 worker coworker model, say we didn't have this 

11 information on them and then wanted to apply a 

12 coworker model would it actually cover their 

13 best estimate intakes? And that's one, I guess, 

14 litmus test you can always put out there. And 

15 it's sort of limited to claimants in most cases. 

16 Because you need information about employment 

17 periods and what type of intakes they actually 

18 experienced.

19 But there are ways to try to get a 

20 handle on if, you know, maybe it's not, maybe the 
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1 hypothesis testing won’t give you enough 

2 granularity to tell, but sort of a second tier 

3 way to get around the problem, or to get a handle 

4 on the problem rather, is to perform intake 

5 analysis for a group of workers who you feel may 

6 have the potential to be way up there.

7 And then compare that back to what 

8 your all worker distribution would have assigned 

9 them, then it's either going to cover what they 

10 actually experience or it won’t.

11 DR. NETON: Well, I mean, all that 

12 proves is that you demonstrated that there's 

13 workers in the tails of distribution. I mean --

14 MR. BARTON: But if there's a 

15 consistent job title that was up there above the 

16 95th.

17 DR. NETON: And remember, these 

18 were the monitored workers, not the unmonitored 

19 workers. Just because there's a monitored 

20 worker up there with a certain job title that's 
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1 in the upper tail doesn't mean all the 

2 unmonitored workers --

3 MR. HINNEFELD: With that job title.

4 DR. NETON: -- with that job title 

5 were in that upper tail as well. I mean, you've 

6 got to --

7 MR. STIVER: It might provide a 

8 proof of principle, if you saw that you have some 

9 monitored workers who would be far above and 

10 beyond the 95th percentile.

11 CHAIRMAN MELIUS: Yes. I actually, 

12 I think that's --

13 MR. STIVER: I mean, that's how --

14 (Simultaneous speaking.)

15 CHAIRMAN MELIUS: If everybody in 

16 that upper tail you have from their CATI 

17 interviews or whatever, that did certain jobs or 

18 whatever, I mean --

19 MR. HINNEFELD: In the Fernald 

20 example, what that exercise showed was that this 
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1 group of monitored contractors, it would not 

2 have been suitable to do their dose 

3 reconstruction with a coworker, which you would 

4 not have done anyway, because you had their 

5 monitoring results.

6 The question at Fernald was, in this 

7 instance, Fernald saw these exposed 

8 contractors, and sufficiently it applied a 

9 monitoring program.

10 But there wasn't a lot of confidence 

11 that that was a routine occurrence, that it was, 

12 quite likely, since there were so few pockets of 

13 contractors being monitored up until the 80s, 

14 it's likely that that consideration didn't occur 

15 consistently for contractors.

16 And so there was no confidence that 

17 the contractors -- I think, you know, it was 

18 cool, it was a nice exercise. All it proved was 

19 that these monitored people shouldn't get the 

20 coworker model, but they wouldn't get it anyway.
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1 MR. BARTON: I guess the assumption 

2 would be that people who were doing the same 

3 types of jobs that weren't monitored would have 

4 the coworker model applied to them.

5 MR. HINNEFELD: Yes. But, you 

6 know, we -- let's don't divert into the Fernald 

7 discussion. Because we can go a ways on that.

8 CHAIRMAN MELIUS: But you use your 

9 coworker models to fill in, you know, gaps in 

10 your monitored, mostly monitored workforce.

11 MR. HINNEFELD: I'm trying to, I 

12 think, Jim, your discussion here is that that's 

13 what we're interested in, what are the gaps in 

14 my monitored workforce?

15 And there might be multiple, there 

16 might be different gaps. And each different gap 

17 deserves a different treatment. Is that kind of 

18 where you’re at?

19 CHAIRMAN MELIUS: Yes, yes.

20 MR. HINNEFELD: It may deserve its 
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1 own treatment.

2 CHAIRMAN MELIUS: You need to 

3 consider it or you, and how big those gaps are 

4 is, I think, has a large impact on --

5 MR. HINNEFELD: Okay.

6 CHAIRMAN MELIUS: -- the validity of 

7 your model that you're going to be using. A 

8 coworker model that fills in a small gap is not 

9 as potentially problematic as a coworker model 

10 that fills in big gaps or covers, and again, it's 

11 all site specific.

12 MR. HINNEFELD: But, well, as a 

13 practical matter, I'm trying to decide what are 

14 the characteristics you used to define the gap. 

15 And I think that's where Jim was going earlier 

16 on.

17 Because depending upon what 

18 characteristics and how many you decide to use 

19 to define the gap, you could have a very large 

20 number of small gaps.
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1 And so there has to be some thought, 

2 I think, at some point, probably not at the 

3 meeting today, but at some point some thought put 

4 into what are the criteria that would be used if 

5 you're starting to evaluate what is the gap. So 

6 that's one thing. And then the second 

7 thing that concerns me, I'm trying to envision 

8 how this would work, is if you, you know, how will 

9 you know exactly the existence of the gap?

10 DR. NETON: Exactly.

11 MR. HINNEFELD: Because you will 

12 have, if you have the bioassay records for, you 

13 know, the entire database from places we have 

14 that for some places, you'll have the monitored 

15 population.

16 But you don't have the unmonitored 

17 population in its entirety. You have, if you 

18 have claims that don't have monitoring data, 

19 then you have a sampling of the unmonitored 

20 population from your claimants who don't have 
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1 monitoring data.

2 So you may be able to conclude some 

3 things from that. Now, that is a sampling. I 

4 didn't say it was a random sampling.

5 I don't know that you're going to 

6 believe it's not a random sampling, but you have 

7 a sampling. So it's not entirely clear to me how 

8 you identify your unmonitored population which 

9 is a precursor to identifying your gaps.

10 So there're some practical 

11 complications that, I think, would have to be 

12 worked through with some thought and certainly 

13 by people smarter than me. But there are some 

14 things that have to be thought about in terms of 

15 doing.

16 CHAIRMAN MELIUS: But I think you 

17 want to classify your gaps. You have gaps that, 

18 among the monitored workforce you have gaps by 

19 year, or within a year, you know. Then not 

20 everybody gets monitored every year. They miss 
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1 it for some reason, or there's a year where it 

2 doesn't get done.

3 MR. HINNEFELD: Oh, yes, I haven't 

4 gotten to that.

5 CHAIRMAN MELIUS: Yes. So you've 

6 got that. And that's where you're using your 

7 coworker model there, right, to fill those gaps? 

8 Or do you --

9 DR. NETON: No.

10 CHAIRMAN MELIUS: What do you do, you 

11 ignore the year?

12 DR. NETON: No. We have a coworker 

13 model that will have multiple data points in that 

14 year. But a person, well, you bring up an 

15 interesting point. Because if the person is 

16 only monitored -- he has monitoring data, we are 

17 going to reconstruct his dose using just that 

18 monitoring data.

19 CHAIRMAN MELIUS: But what if he's 

20 missing three years of monitoring data?
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1 DR. NETON: With three years, he'd 

2 probably end up with some coworker.

3 CHAIRMAN MELIUS: Okay. So, what 

4 I'm saying, that's one consideration. And then 

5 Stu brought up another one. Then you have the 

6 unmonitored populations.

7 And then the question is can you, you 

8 know, how do you apply your coworker model 

9 criteria? And that's probably mostly what 

10 we're trying to talk about.

11 But there can be big gaps that are the 

12 same. If it's a ten year gap or whatever, or a 

13 year where there's very little monitoring data, 

14 and it seems to be, you know, much higher than 

15 the previous year, but it's all situational.

16 And so you say why don't you take a 

17 look at that. If they're totally unmonitored, 

18 then you're going to have to go back to your 

19 interviews and try to characterize who those 

20 people are and so forth.
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1 DR. NETON: But the general sense is 

2 there are not huge yearly, multiple year gaps for 

3 people who I would consider heavily exposed 

4 workers, the ones that were chemical operators, 

5 you know, the ones that were working in the 

6 process area apparently routinely.

7 And I don't think that's an issue. I 

8 do think that we've done similar analyses where 

9 you can go and take the job categories of the 

10 unmonitored workers in the claimant population 

11 and do an analysis and say where would they fall 

12 in the coworker model.

13 I'm trying to think this through. I 

14 don't know. I lost my thread on that.

15 But Stu is right, you don't know who 

16 the monitored population is, really.

17 CHAIRMAN MELIUS: Yes.

18 DR. NETON: Except for the gaps. I 

19 mean, maybe there're gaps in some of the worker 

20 -- but it's really all based on claimant data, 
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1 not based on, you don't know who was unmonitored, 

2 I guess. That's the problem.

3 CHAIRMAN MELIUS: Yes. Which makes 

4 it even more problematic to use a coworker model 

5 there. And I think we, that's why I'm saying, 

6 you want to separate out your application of 

7 coworker models.

8 What’s the number of unmonitored 

9 workers, which is always an unknown.

10 DR. NETON: Yeah. We have time to 

11 think about this a little more.

12 DR. MAKHIJANI: I still think it's, 

13 at Savannah River we had, when we dealt with 

14 external dose always a less difficult or easier 

15 issue on the stratification question.

16 I remember there was a NIOSH exposure 

17 ratio to be used for construction workers 

18 compared to non-construction workers.

19 DR. NETON: For external 

20 monitoring.
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1 DR. MAKHIJANI: External monitoring 

2 and unmonitored workers. And when we, when 

3 Steve Marschke actually devised this procedure.

4 And we came up, we agreed that in the 

5 vast majority of cases where you find for 

6 pipefitters it wasn't. And it was a useful, it 

7 doesn't tell you that the unmonitored workers, 

8 you know, it doesn't tell you about the universe 

9 of unmonitored workers. But it does tell, 

10 it gives you some confidence that if the 

11 unmonitored workers were like the monitored 

12 workers or less exposed than that, then you have 

13 some confidence in what you're doing. If you 

14 don't know that, of course, then it's very 

15 difficult.

16 DR. NETON: That was for external 

17 dosimetry. And the coworker models are treated 

18 somewhat differently there.

19 I think in the external, the 

20 unmonitored, people who are unmonitored for 
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1 external exposures are given the full 

2 distribution whether they were the, you know, 

3 were highly exposed workers or not. There's 

4 reasons for that. And we've discussed that.

5 DR. MAKHIJANI: I don’t remember 

6 that.

7 DR. NETON: Yes. So they don't get 

8 the 95th percentile. And to me that's, the 95th 

9 percentile the reason we do that for internal is 

10 because it's much more complicated and hard to 

11 factor out.

12 DR. MAKHIJANI: I didn't remember 

13 that. Thank you.

14 DR. NETON: But I'm still having a 

15 little trouble working through this. Because 

16 once you do, if you do show there are 

17 differences, then we need to talk about the next 

18 issue, is what's the significant difference?

19 How does that, you know, how does 

20 that affect -- because as, you know, we're not 
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1 talking about that paper yet, but there's 

2 clearly examples in multiple, multiple 

3 instances that it has to be a factor of two to 

4 produce a PC that's greater than just using the 

5 95th percentile.

6 So I'm struggling with that. I 

7 don't know, you know, you can stratify and give 

8 a person 25 percent more dose with the full 

9 distribution, but they're going to get a lower 

10 PC at the end of the day.

11 And is that really where we want to 

12 go with it? I don't know. I mean, it doesn't 

13 make sense to me, especially if you can't 

14 statistically show that they're good. You 

15 know, that's probably for further discussion.

16 CHAIRMAN MELIUS: But I think just 

17 back to the sort of monitored/unmonitored issue, 

18 I think it's also, maybe we have to think of it 

19 in terms of types of coworker models.

20 There's sort of a, you know, the 
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1 individual gap one. The other one, which at 

2 least comes to mind in a lot of sites, is that 

3 where you have early years where there's very 

4 sparse data, and then we wrestle with whether the 

5 coworker, how far back can the coworker model 

6 apply?

7 Because we have a handful of workers 

8 that were monitored earlier, you know, 

9 relatively small proportion. And then it gets 

10 more robust.

11 I hope Wanda is not listening. But 

12 as time goes by and we get, and then the question 

13 is how far do you go back? And that's sort of, 

14 usually you're assuming that, I mean, they're 

15 unmonitored for a different, you know, for one 

16 reason.

17 Then you have this whole other 

18 population that's just unmonitored. And the 

19 question is what's appropriate and applicable 

20 for them?
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1 And I would think that, you know, at 

2 least my criteria, or tolerance or whatever you 

3 want to call it for, you know, how robust the 

4 coworker model has to be to be different in those 

5 situations.

6 I think if you're filling in a one or 

7 two year gap for certain individuals, that 

8 doesn't, you know, it's probably very reasonable 

9 to do.

10 But if you're filling back in a year 

11 or two, early on or, you know, that's more 

12 problematic. And then you go to apply to the 

13 totally unmonitored population, and then I think 

14 the bar gets pretty high.

15 And again, it may depend on what that 

16 unmonitored, what possibilities or what 

17 potential that unmonitored population had for 

18 being exposed.

19 So again, you're do the clerical --

20 the people who worked out front that, you know, 
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1 that's going to be, that's the level of exposure 

2 involved is also important.

3 DR. NETON: I have to think about 

4 this.

5 CHAIRMAN MELIUS: Yes.

6 DR. NETON: I'm just having trouble 

7 coming up with a predetermined set of criteria. 

8 I mean, you can never predetermine, but you have 

9 to have some basis for saying I think that I 

10 should stratify here somehow.

11 You can’t just say let me do 50 tests. 

12 You know, it just doesn't make any sense. I 

13 mean, statistically you'll find some chance, 

14 happenstance. But it's --

15 MR. KATZ: Excuse me, there's 

16 someone having a family conversation perhaps in 

17 the background. Anyone on this line who's not 

18 addressing the group should be muted.

19 If you don't have a mute button on 

20 your phone, please press *6 to mute your phone. 
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1 But I'm just worried about other people on the 

2 phone in particular, we can hear ourselves, but 

3 whether they can hear us. Thank you.

4 DR. NETON: And I have to think about 

5 that some more. I've always agreed it needs to 

6 be done. I just don't know --

7 CHAIRMAN MELIUS: No, that's fine.

8 DR. NETON: -- I don't know how to do 

9 the -- you know -- in advance.

10 CHAIRMAN MELIUS: So we've spent 45 

11 minutes on 3.0.

12 DR. NETON: Yes. 3.0 is going to be 

13 tough. 3.1, gets into the time interval. And 

14 I think we’ve already talked about that to some 

15 degree. You can't assume that all conditions 

16 stay the same over periods of time.

17 The evaluation stratification, which 

18 is probably the section that needs the most work, 

19 because I wasn’t quite sure where to go with this. 

20 You know, we acknowledge that the coworker model, 
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1 the all monitored workers contains, more than 

2 likely, multiple distributions.

3 It's just because the GSDs tend to be 

4 large, and it's a spread. So you can try to 

5 stratify at some point. And then this is where 

6 we get into using the 95th percentile versus the 

7 full distribution.

8 And we spent a fair amount of time 

9 looking at this. And it's not just dose 

10 dependent. It really is PC dependent to the 

11 point where, if you can look at every single 

12 cancer model, the lowest difference is a factor 

13 of two.

14 So you can stratify and say, okay, I'm 

15 going to give this guy a 25 percent more dose at 

16 the geometric mean with the same GSD or slightly 

17 different.

18 The end PC result will be 50 percent 

19 less. Not 50 percent, but a lot less because of 

20 the way the 95th percentile plays against the 
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1 full distribution.

2 We've done a very detailed analysis 

3 of that which we really may not get to today. So 

4 then that brings in the question when should one, 

5 even if you can stratify and you can develop 

6 multiple distributions that have different 

7 geometric means and standard deviations, is it 

8 advisable, is it claimant-favorable or not? 

9 When should one do that?

10 And I am of the opinion right now that 

11 a statistical test, pure statistical tests based 

12 on the numbers is probably not the place we need 

13 to end up.

14 CHAIRMAN MELIUS: Where do you think 

15 we should end up?

16 DR. NETON: Well, I proposed in my 

17 write-up that, unless there's a factor of two 

18 difference in the geometric means, that we 

19 shouldn’t stratify. Because otherwise, with a 

20 fair amount of certainty, the Probability of 
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1 Causation would be less for that person.

2 MR. BARTON: You indicated the GSD 

3 would obviously play into that too right? It 

4 wouldn’t just be the geometric means you compared 

5 with --

6 DR. NETON: I compared geometric 

7 means with the same GSD. And they tend to be 

8 similar to GSDs when you stratify. In fact, when 

9 you stratify and the geometric mean goes a little 

10 higher, it seemed to me the GSD goes down a little 

11 bit. Because you shrunk, you know, that 

12 population.

13 And the converse is true. When the 

14 geometric mean becomes a little lower the GSD 

15 could become lower. But in general, the GSD 

16 stayed fairly stable.

17 And so for similar GSDs, I've run the 

18 calculations, Dan Stancescu's actually run the 

19 calculations many different ways. It's a factor 

20 of two difference.
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1 So my position, at this point, would 

2 be that we can do our statistical analysis. If 

3 they're not statistically different, then don't 

4 stratify. If they are, go ahead. But if it's 

5 less than this factor of two, you probably 

6 shouldn't.

7 DR. MAKHIJANI: If what is less than 

8 a factor of two?

9 DR. NETON: The geometric means, the 

10 difference in geometric means. An example I 

11 provided, it was two distributions. I think 

12 they were real distributions. One has geometric 

13 means 24 percent higher.

14 If you run the PC calculation for the 

15 most favorable cancer, it's a factor of 1.6 

16 lower. It's purely a matter of the fact that you 

17 are using the 95th percentile versus the full 

18 distribution. And that's just a fact.

19 It's not exactly that, because it’s 

20 hard to control all the parameters, we try to do 
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1 the analysis such that we maximize our chance to 

2 show that, you know, little difference. So 

3 that’s to me food for thought.

4 CHAIRMAN MELIUS: Yes. I think the 

5 more problematic area is when you're doing 

6 stratification, or it's comparing the dose 

7 monitoring approaches used with the -- how 

8 different do those have to be to undermine the 

9 validity of your comparisons?

10 So, you know, the routine versus, 

11 particularly with the incident basis, or you have 

12 some mixed approach that's used for part of the 

13 population. And that's been the construction 

14 worker --

15 DR. NETON: Yes.

16 CHAIRMAN MELIUS: -- issue. And I 

17 think that's more of an issue.

18 DR. NETON: See, in my mind that 

19 construction issue falls more in the realm of is 

20 it going to be incident-based or not. Can you 
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1 do it all? That's like can I stratify or not?

2 When we start comparing similar 

3 programs, like both routine programs, and one's 

4 a chemical operator and one is, you know, and you 

5 compare the chemical operator distribution to 

6 all monitored workers, you say, wow, it's a --

7 CHAIRMAN MELIUS: Yes, yes.

8 DR. NETON: -- 30 percent higher for 

9 routines, okay. What is that going to get you 

10 in terms of PC values, it’s going to lower the 

11 PC values, even if it's 30 percent higher.

12 So I don't know. That's why I keep, 

13 sort of broken record, I keep harping on it. The 

14 95th percentile sort of mitigates that by saying, 

15 well, you're trying to be, you know, favorable 

16 here.

17 We don't know exactly what it is. 

18 It's plausible the guy is up this high. It's 

19 plausible he's a little higher, but even if he 

20 is a little higher it's not going to be favorable 
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1 for him to use a little higher values. That's 

2 where I'm at. It's a very complicated 

3 situation.

4 MEMBER ROESSLER: It's very 

5 complicated. And it's so complicated that I 

6 have to ask a question, if I can even ask it.

7 When you talk about it, you're saying 

8 that if it can be shown that the use of the full 

9 distribution in the stratified subset is more 

10 favorable than using the 95th percentile, the 

11 general distribution, then you should use the 

12 full distribution. Can you actually when you are 

13 doing a dose reconstruction determine that and 

14 then go one way or the other?

15 DR. NETON: I think theoretically 

16 you could. But it'd be so unbearably 

17 complicated it would -- I think you could set 

18 guidelines, like I say a factor of two --

19 CHAIRMAN MELIUS: Yes.

20 DR. NETON: -- in my analysis. You 
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1 know, it can be a factor, as high as a factor of 

2 four for some cancers. The lowest cancer, the 

3 lowest difference was a factor of two, I think, 

4 for urinary cancers other than the bladder or 

5 something like that.

6 And so what I'm saying is, unless you 

7 can demonstrate that there's this huge 

8 difference in the geometric means, then it's not 

9 worth stratifying at all.

10 MEMBER ROESSLER: Yes, okay.

11 DR. NETON: So you can pick some 

12 number. Well, there's two situations. One is, 

13 the issue is that the statistics are not good 

14 enough.

15 You can't see small differences, 

16 statistically. You can't demonstrate that 

17 there're statistically different for small 

18 differences. It has to be a fairly large 

19 difference.

20 What I'm saying is, well, you don't 
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1 have to see small differences because, from a 

2 Probability of Causation point of view, a factor 

3 of two is not going to help the claimant at all 

4 by increasing his -- increasing his dose by a 

5 factor of two will not help him in his Probability 

6 of Causation outcome.

7 Now that's not hard and fast, that's 

8 not perfect. But that's a very good 

9 approximation, in my mind.

10 MR. BARTON: Jim, you lost me a 

11 little bit there. Because when I read this final 

12 sentence in Section 4, it sounds like what you 

13 were proposing was you have your all worker 

14 distribution. You want to know whether you need 

15 to stratify or not.

16 I thought what you were trying to say 

17 is, okay, we suspect some group of workers need 

18 to be tested for whatever reason, interviews or 

19 whatever.

20 And so we pulled them out and then we 
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1 create a separate distribution. And I thought 

2 what you were saying was, from a PC standpoint, 

3 you compare the 95th percentile of all worker to 

4 the PC generated from the full distribution of 

5 this new stratified set.

6 DR. NETON: Right.

7 MR. BARTON: And based on that 

8 comparison, you can say that the stratification 

9 is even warranted, if it would benefit claimant.

10 But then I thought I just heard you 

11 say, well, the results on this is we're just going 

12 to compare the GMs at the factor of two. But you 

13 would actually build the two strata first to sort 

14 of test it?

15 DR. NETON: You have to build a two 

16 strata.

17 MR. BARTON: Okay.

18 DR. NETON: But what I'm saying is do 

19 you do a statistical test or do a practical test 

20 based on the PC.
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1 MR. BARTON: Okay.

2 DR. NETON: I mean, what I'm saying 

3 is, you can do it. And that kind of example I 

4 provided is a 24 percent difference in geometric 

5 mean. If you run the PC calculation using that 

6 value as a full distribution, as opposed to the 

7 95th, the PC is lower, much less.

8 MR. BARTON: Okay, I understand.

9 DR. NETON: Eleven percent versus 20 

10 percent for that particular case. So, you know, 

11 trying to be claimant-favorable on one side, you 

12 end up hurting the person's chances on the other 

13 side.

14 So, I don't know. I don't have a real 

15 answer for that right now. I clearly wanted to 

16 point that out. I brought this up at the last 

17 meeting, that this is how we behave, and we did 

18 the analysis.

19 And my intuition was correct, that it 

20 does produce lower PC values. So, you know, we 
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1 went ahead and stratified just blindly. Because 

2 the number was higher, you ended up not favoring 

3 the claimant very much.

4 CHAIRMAN MELIUS: Well, that's why 

5 we started down that road to begin with, what 

6 level of difference makes a difference.

7 DR. NETON: Right. And finally I 

8 came up with this thing --

9 (Simultaneous speaking.)

10 CHAIRMAN MELIUS: So, yes. And 

11 again, that's why I'm sort of trying to sort of 

12 front-load our meeting today.

13 I think that the key is going to be, 

14 where we're going to need to do the work is, you 

15 know, is up front in how we approach developing 

16 these models and so forth.

17 And then, you know, how we look at the 

18 monitoring things and how it's going be simple, 

19 too routine, one routine versus another, it's 

20 going, I think, be a much more mixture of that.
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1 And how much of a mixture is going to 

2 -- and I don't know if statically we can deal with 

3 that. But I think there's probably some, you 

4 know, at least guidance we can give in terms of 

5 making sure that's looked at, and evaluated and, 

6 you know, addressed in the context of that site.

7 Because, you know, we're going to be 

8 different, we've got different sites, and 

9 different monitoring programs, and different 

10 exposures and so forth.

11 DR. NETON: I think we're, and I'm 

12 not sure. A lot of disagreement among us on most 

13 of these points, and I agree we need to stratify 

14 or at least evaluate. How much we do that is 

15 still a little bit cloudy in my mind.

16 But to close this all out, we're going 

17 eventually have to come to a decision on whether, 

18 how we do the final comparison to see what 

19 stratification is warranted. And prior to that, 

20 we need to make a decision on how the coworker 
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1 models themselves are constructed.

2 CHAIRMAN MELIUS: Yes. No, no, no. 

3 And I don't think we can do the, make the final 

4 decision, so to speak, until we address the 

5 other.

6 DR. NETON: Okay, that's fine.

7 CHAIRMAN MELIUS: I think --

8 DR. NETON: I feel --

9 CHAIRMAN MELIUS: You know, I don't 

10 think we're, I don't think we're far apart on that 

11 --

12 DR. NETON: Okay.

13 CHAIRMAN MELIUS: -- this situation. 

14 Arjun, you --

15 DR. MAKHIJANI: Well --

16 CHAIRMAN MELIUS: Good, conquered.

17 DR. MAKHIJANI: Yes, I'm okay with 

18 it.

19 CHAIRMAN MELIUS: We'll wait until 

20 next meeting.
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1 DR. NETON: That finishes out 

2 my thoughts.

3 CHAIRMAN MELIUS: Three weeks, three 

4 weeks, comments.

5 DR. NETON: That'd be great. And 

6 again, all comments are helpful, please. I 

7 don't like doing this in a vacuum.

8 DR. MAKHIJANI: I do have one 

9 question. This factor of two, not making much 

10 of a difference in the Probability of Causation. 

11 How, we had a limited number of cancers, as I 

12 remember you didn't have very many cases to look 

13 at.

14 DR. NETON: I mean, I look at a 

15 lot of cancers. We looked at all 33 cancer 

16 models.

17 DR. MAKHIJANI: Oh, you took that 

18 certain dose information, then looked at all the 

19 cancers?

20 MR. HINNEFELD: Yes, it was one 
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1 particular stratified subset of a broader 

2 population.

3 DR. NETON: Well, that was the 

4 example. But on a generic basis, what we did is 

5 I said, okay, let's have a coworker model that 

6 has a GM of one and a GSD of three, okay.

7 How much higher would that model have 

8 to be to exceed the PC used in the 95th percentile 

9 of that distribution? And that's a factor of two 

10 at the least.

11 It's up to a factor of four for some 

12 cancers, assuming the GSDs are the same. Now, 

13 if the GSD is higher in the stratified model, then 

14 that's going to be a little lower, as you saw in 

15 the example, 1.6.

16 DR. MAKHIJANI: Right.

17 DR. NETON: But it's in that ball 

18 park. I'm not saying it is a factor of two, I'm 

19 saying my analysis is a factor of two. It's 

20 certainly in that ball park, in my opinion. 
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1 Although, again, I think I pretty clearly pointed 

2 out this wasn't exhaustive among all --

3 DR. MAKHIJANI: No, no, no. I think 

4 that analysis you did is very useful.

5 CHAIRMAN MELIUS: Yes.

6 DR. MAKHIJANI: So I'm not raising an 

7 issue about that.

8 DR. NETON: Yes.

9 DR. MAKHIJANI: I just was trying to, 

10 I didn't remember that you had actually looked 

11 at all the cancers.

12 DR. NETON: I did. We did analysis 

13 of GM of one, GSD of three for all 33 or whatever 

14 you call them, breast cancer too. And we did all 

15 of them. And I said, and we ranked them. It's 

16 in that paper.

17 MEMBER BEACH: Jim, that's the --

18 DR. NETON: Yes, that's table.

19 MEMBER BEACH: Oh, okay, that's what 

20 I thought.
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1 DR. NETON: And the smallest 

2 difference was about two. I think it was urinary 

3 tract cancers.

4 DR. MAKHIJANI: Presumably there 

5 would be some way of using that analysis to give 

6 you a little bit more elbow room. 

7 DR. NETON: That was my whole 

8 thinking, exactly what I was trying to do. You 

9 know, again, you can't see, statistically you 

10 can't see 20, 30, 40, or 50 percent differences. 

11 And you've already kind of decided on that.

12 I think Harry made his point very 

13 clear at the last meeting, you are comparing two 

14 geometric means with large GSDs, you need big 

15 differences in GMs to see that. And I'm saying 

16 you probably don't have to because of this, this 

17 issue. Or you could, you could do --

18 DR. MAKHIJANI: You need a very large 

19 increment dose to make up. When you get close 

20 to 50 percent.
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1 DR. NETON: Yes. It puts the 

2 hundred millirem analysis to shame.

3 And so it gets even more favorable 

4 when you do -- remember, this is for one year. 

5 When you start doing multiple years, it becomes 

6 more spread apart because of correlation issues, 

7 in some respect. You're sampling the 95th 

8 percentile as a constant every single time.

9 Yes. And again, don't get me wrong. 

10 I'm not saying it is a factor of two. I'm saying 

11 it's a fairly large --

12 DR. MAKHIJANI: No, no, no. I heard 

13 you. I am actually saying thank you, you know, 

14 I didn't remember how thorough you had actually 

15 addressed it.

16 DR. NETON: And we used chronic alpha 

17 exposure, which I think tends to also increase 

18 the distribution. But, you know, that aside all 

19 the stuff we just talked about before still is 

20 valid. Because you still need to, you need 
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1 pieces or parts here before you get to that final 

2 analysis.

3 DR. MAKHIJANI: Right. This just 

4 gives you elbow room at the end.

5 DR. NETON: That's my thinking at 

6 this point. You get a little bit of, I guess, 

7 elbow room or, a little bit of variability in 

8 there that --

9 DR. MAKHIJANI: I mean, it's not some 

10 other negligible issue. So if you get to that 

11 last step, it makes your co-worker model results 

12 look more robust in light of the ultimate 

13 decision.

14 DR. NETON: Well, unfortunately, it 

15 has to take it about three steps further, but we 

16 won't get into that today. But this is not just 

17 based on one single year. You know, this would 

18 be based on a coworker model that fits a chronic 

19 exposure oftentimes over a ten year period.

20
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1 I could show you this, but the 

2 distribution that's applied in the dose 

3 reconstruction is not the distribution of the 

4 bioassay samples for one year. It's the 

5 distribution of the chronic exposure intake over 

6 a ten year period oftentimes in that GSD. Because 

7 the analysis is still valid.

8 But that would only affect one year. 

9 If you had a difference even in one year, you 

10 still need to look at all ten years in the chronic 

11 exposure model to see if that actually changes 

12 the chronic intake which is what goes --

13 DR. MAKHIJANI: Right.

14 DR. NETON: The dose from that goes 

15 into the model.

16 DR. MAKHIJANI: Yes.

17 DR. NETON: I have some slides on this. 

18 But I think the analysis is pretty informative 

19 about at least decision making for 

20 stratification once we've done all these. Okay?
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1 DR. MAKHIJANI: Yes.

2 DR. NETON: Okay.

3 CHAIRMAN MELIUS: Since Ted didn't 

4 put an end time on the agenda --

5 MR. KATZ: There's no end --

6 CHAIRMAN MELIUS: There's no end 

7 time.

8 DR. NETON: We have to stay here

9 until midnight.

10 (Laughter.)

11 CHAIRMAN MELIUS: And they've locked 

12 the doors. Do you want a short break? And then 

13 we can move on to the next two. Or do you want 

14 to just go ahead? Seriously I had planned on 

15 going to 5:00.

16 DR. NETON: Yeah, I could keep going.

17 MEMBER ROESSLER: Let's plug ahead, 

18 yeah.

19 CHAIRMAN MELIUS: Okay. Coworker 

20 model?
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1 MEMBER ZIEMER: How long are we 

2 breaking for?

3 CHAIRMAN MELIUS: No. We're not 

4 breaking.

5 MEMBER ZIEMER: Oh, you're not 

6 breaking. Okay.

7 CHAIRMAN MELIUS: Yeah. We got 

8 voted down. No one would believe me that we're 

9 going to stay here until 10:00. So, no, we're 

10 going to break at 5:00. But we'll finish up in 

11 about an hour.

12 DR. NETON: Okay, coworker. So --

13 CHAIRMAN MELIUS: We just talked 

14 about a good portion of this.

15 DR. NETON: Yeah. Which one do you 

16 want to talk about, the time-weighted average or 

17 the strata comparison?

18 MEMBER BEACH: Let's do the 

19 time-weighted. If we get to go --

20 DR. NETON: Okay.
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1 CHAIRMAN MELIUS: I was going to say 

2 do the strata because we've been talking about 

3 it --

4 MEMBER BEACH: Okay, that works too.

5 CHAIRMAN MELIUS: -- to some extent, 

6 because I think it'll be --

7 MEMBER BEACH: Short?

8 CHAIRMAN MELIUS: I don't know.

9 DR. NETON: It should be pretty short.

10 MR. HINNEFELD: Jim didn't think we 

11 spent much time on the first one.

12 DR. NETON: I thought the first one 

13 we breezed through real quickly.

14 CHAIRMAN MELIUS: I had other ideas. 

15 I'm sorry.

16 DR. NETON: No, that's fine. All 

17 right. All right, this is Daniel Stancescu, 

18 who's on the phone, I hope, still. It’s going 

19 on 6 o’clock where he is. I think he's going to 

20 stick around. Anyway, he did much of the 
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1 analytical work behind this, but I'll take full 

2 blame for conceiving of the concept.

3 Anyway, RPRT-53, which we talked 

4 about a couple meetings ago, talks about a 

5 statistical approach for evaluation 

6 stratification. It's a two-tiered evaluation 

7 where the stratified distributions are first 

8 compared on a year-by-year basis and look for a 

9 difference in those strata.

10 And if any individual year or 

11 increment that's evaluated, whether it's 

12 something other than a year, are different, you 

13 still need to apply what we call a practical 

14 significance test, which is what I just sort of 

15 talked about.

16 I'm applying this to a chronic 

17 exposure model over a, most of the time, 

18 multiple-year period. Does that make a 

19 statistical difference to the chronic exposure 

20 model?
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1 The test that's used to look at 

2 differences between strata are the Monte Carlo 

3 permutation test and the Peto-Prentice test, and 

4 we’ve talked about those. But the issue is, you 

5 can't really see very small differences between 

6 distributions.

7 And I got to thinking about this, and 

8 I broached this subject at the last Working Group 

9 meeting, that in reality, though, we don't 

10 compare a full distribution to full 

11 distribution. In practice, we'll apply the 95th 

12 percentile. If it's stratified, then you go and 

13 use the full distribution.

14 Well, we got to thinking about, well,

15 what difference would that make, practical 

16 difference, in terms of a Probability of 

17 Causation outcome? So we went and explored the 

18 relationship between the PC generated for a 

19 stratified model using a full distribution and 

20 the 95th percentile.
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1 The paper describes the sort of input 

2 parameters that were selected and why. I won't 

3 bore you with those details. You can look at 

4 them. But there are caveats. We had to make 

5 certain assumptions and we've outlined or 

6 described, I think pretty well, why we picked 

7 what we did.

8 To get to the bottom line, though, if 

9 you look at the table that I think Josie was just 

10 showing, this is a table of all the IREP cancer 

11 models. And we put into the IREP cancer model 

12 either a full distribution, and got a PC outcome 

13 -- and the distribution was a geometric mean of 

14 one and a GSD of three. These could be any units, 

15 but for comparison purposes we just stuck with 

16 one and three.

17 And then we calculated what the 95th 

18 percentile that distribution would be. And that 

19 is 6.09.

20 So in one analysis, for example 
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1 female genitalia, the first cancer here, we put 

2 6.09 into the IREP input model and got a PC 

3 result. And then we reran the analysis and put 

4 in the stratified model, which would be a 

5 geometric mean of one and a GSD of three, and the 

6 stratified model would have to have a geometric 

7 mean four times that of the geometric mean of one 

8 in order to get the same PC value.

9 So that's the worst-case analysis. 

10 And there is a distribution of PCs because all 

11 the PC models have different uncertainties 

12 associated with them. The bottom line is, if you 

13 get down to the last cancer, the lowest one was 

14 urinary organs excluding the bladder, and to get 

15 the same PC as the 95th percentile, the full 

16 distribution would have to have a geometric mean 

17 of 2.07 and a GSD of three to get the same PC as 

18 putting in 6.09, which is what we would use.

19 DR. MAKHIJANI: GSD of what?

20 DR. NETON: Three. Now, we've done 
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1 this for other GSDs: four, five, six. Not gotten 

2 as high as six, but four. I'm not sure we did 

3 five. It seems to track with GSD. It doesn't 

4 really matter what the GSD is on the distribution 

5 as long as they're equal.

6 Once you start getting into 

7 discrepancies in the GSDs, these values will, of 

8 course, change. If the GSD is larger for the 

9 stratified model, then the multiplier would be 

10 somewhat lower.

11 And the example I provided is these 

12 two cases here. There's a full and a stratified 

13 model. You could see that the GM is 0.75 with 

14 a GSD of 4.05, and stratified had a GM of 0.9 with 

15 a GSD of 3.7.

16 We compared those and I believe the 

17 analysis showed that the PC would be 1.6 lower. 

18 I got the numbers here.

19 DR. MAKHIJANI: 1.6 percent?

20 DR. NETON: No. Hang on, let me get 
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1 to the values. Where's my example? Yeah. So 

2 when you run that -- do I have the PCs listed in 

3 here? Daniel, are you on the phone? Oh, here 

4 it is.

5 At the 99th percentile, the PC was at 

6 the -- hang on -- yes, the first run used 7.51 

7 as the input term. The second one used the full 

8 distribution at the 99th percentile, which is 

9 where we select the values. The NIOSH output 

10 results were 12.2 percent for the stratified 

11 subset and 20 percent for the 95th percentile.

12 So if you use the 95th percentile, 

13 even though that geometric mean is 24 percent 

14 higher, you get a 20 percent PC. For using the 

15 95th percentile, you only get a 12 percent PC for 

16 the stratified model even though it's got a much 

17 larger GM and a slightly higher GSD.

18 So I think this kind of analysis can 

19 be done somewhat repeatedly for many different 

20 examples and you come up fairly close.
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1 So I'm not saying it's a factor of 

2 two. It's a large difference. If the GSDs are 

3 the same, it's a factor of two or more. So that's 

4 the end result of that analysis.

5 DR. MAKHIJANI: But is it realistic 

6 to assume that the GSDs are saying -- I mean, when 

7 you have stratum like the construction workers 

8 at Savannah River where you have few data points 

9 for that stratum, you're going to have a pretty 

10 big GSD, right? And that's why we had this --

11 DR. NETON: Well, I don't know.

12 DR. MAKHIJANI: -- difficulty 

13 arriving at a conclusion. I don't know. Maybe 

14 Harry or Bob might want to say something about 

15 that, because I don't think I remember enough 

16 about the details this far in time now.

17 MR. STIVER: Yeah, you're talking 

18 about the RPRT-53 analysis that Harry did?

19 DR. MAKHIJANI: Yes.

20 MR. STIVER: Harry, are you on the 
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1 line? Could you say a few words?

2 DR. CHMELYNSKI: Yes, I am, Bob. 

3 I'm trying to remember what kind of GSDs we saw 

4 there, but they often are up in the fours and 

5 fives for these subgroups. I don't know exactly 

6 how that compares, though, with the overall 

7 all-worker models.

8 DR. NETON: Right. I think they can 

9 be higher, but they're in the same ballpark. I 

10 mean, they're not typically, you know, widely 

11 different because it's -- especially if the 

12 geometric mean is higher, you start -- you're 

13 pushing yourself up towards the end of the 

14 distribution and it seems to me that that would 

15 almost tend to lower the GSD.

16 I've seen that in a number of cases, 

17 where if you're pulling out a distribution that 

18 has a higher GM then you've got a more shrunken 

19 down subset to deal with.

20 DR. MAKHIJANI: That seems like a --
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1 it seems difficult to generalize from something 

2 where you're assuming the same GSDs where one 

3 stratum doesn't have many data points.

4 DR. NETON: Yeah, it may be where one 

5 evaluates it on a case-by-case basis from the 

6 general term, in a general sense.

7 This analysis I did is very simple to 

8 do. You stratify and you run the two values at 

9 the 95th percentile versus the models and you 

10 just look. You say how big a difference am I 

11 going to need in order to be more 

12 claimant-favorable? I mean, that could be done. 

13 That could be a test, not maybe the only test but 

14 at least a test.

15 DR. MAKHIJANI: So can you develop, 

16 like, an algorithm that could be very easily 

17 applied?

18 DR. NETON: Oh, I mean, the 

19 calculations are simple. They're very simple 

20 calculations.
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1 MR. BARTON: Jim, if I could ask you 

2 a question. I mean, these examples are 

3 essentially just on a year basis, right?

4 DR. NETON: Right.

5 MR. BARTON: And as you say, when you 

6 generally create a coworker model you combine 

7 multiple years based on patterns you see in the 

8 bioassay data.

9 DR. NETON: Right.

10 MR. BARTON: So I'm wondering how 

11 this might get complicated in picking your intake 

12 regimes because wouldn’t you have to -- I mean, 

13 it seems like you would have to pick the same 

14 intake regime for the all-worker and the 

15 stratified. But when you actually examine the 

16 stratified dataset and the all-worker, you might 

17 not find that that makes a lot of sense to have 

18 the exact same intake intervals.

19 DR. NETON: I'm not following.

20 MR. BARTON: Well, let's say, in 
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1 practice, if you were to use this type of 

2 comparison would you make that comparison for 

3 each year?

4 DR. NETON: No, no. We’re doing it 

5 for one year. I think what would happen is, if 

6 you did it for multiple years, the difference 

7 would tend to get larger. Because you're 

8 putting the 95th percentile in as a constant 

9 every time, and if you put the full distribution, 

10 its sampling, I'm pretty sure that it would be 

11 more disparate.

12 MR. BARTON: Okay. What I'm saying 

13 is --

14 DR. NETON: We could test that.

15 MR. BARTON: There could be the 

16 possibility of a disconnect, because when you 

17 look at the excreted values for a given year the 

18 years that it makes sense to group together for 

19 the all-worker might not be the same as the years 

20 it makes sense to group together for any 
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1 potential stratified. Just wondering. I mean, 

2 that could complicate this. I don't know how 

3 much it would complicate this.

4 DR. NETON: Well, I think you’d have 

5 to do it on a case-by-case basis, like if you had 

6 multiple regimes.

7 CHAIRMAN MELIUS: I mean, I think it 

8 would be worth looking into.

9 DR. NETON: It’s worth looking at. If 

10 you make that comment --

11 CHAIRMAN MELIUS: Yeah. Yeah, just 

12 to --

13 MR. STIVER: Would it necessarily be 

14 a requirement they track together? I mean, if 

15 you’ve already established they can a different 

16 coworker model for the subgroup, it may have some 

17 slightly different --

18 CHAIRMAN MELIUS: I think the 

19 question, it's harder to grasp sort of from a 

20 distance, is how much of a difference does it 
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1 really make, though? And I think that's sort of 

2 Jim's point, is that it doesn't really -- some 

3 of these, you know, quantitatively don't.

4 But it's not to say that we're not 

5 thinking of the right example, the wrong example, 

6 you know, however you want to look at it. There 

7 may be some circumstances where it could occur 

8 where it could, so if people are thinking of 

9 those, let's suggest them.

10 DR. NETON: And, again, this doesn't 

11 really apply to the individual distributions of 

12 bioassay. It applies to the -- I'm going to get 

13 into this in the next paper -- it applies to the 

14 chronic intake model itself. That's where that 

15 difference needs to be demonstrated. Is it 

16 going to change your chronic intake model?

17 DR. MAKHIJANI: Yes, you showed that 

18 on one of your previous --

19 DR. NETON: I have some slides I 

20 think that I'll show that will make that much 
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1 clearer.

2 CHAIRMAN MELIUS: One question I 

3 have, this goes back to some of the earlier papers 

4 and so forth, I think, but why do we have this 

5 array of differences by organ system, this 

6 particular hierarchy? And is this similar to 

7 what we found earlier?

8 DR. NETON: The array?

9 CHAIRMAN MELIUS: Yeah, the 

10 hierarchy of Table 1. You have differences.

11 DR. NETON: Oh, that's just the way 

12 the results came out, I mean.

13 CHAIRMAN MELIUS: So there's no --

14 DR. NETON: There was no rhyme or 

15 reason to that. It was just we ran all cancer 

16 models and we ranked them by their --

17 CHAIRMAN MELIUS: So is that going to 

18 be consistent across different exposure 

19 scenarios, I think, was my question. And are 

20 there something about some of these IREP models 
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1 that might make them more sensitive, so to speak?

2 DR. NETON: Well, yes and no, to be 

3 perfectly honest. These IREP models, of course, 

4 are very complicated mixes of multiple 

5 distributions. And you can’t predict, to the 

6 extent they generate a distribution themselves 

7 of PC outcomes, and how broad that is is really 

8 what drive these numbers.

9 Now, I also used alpha exposure in 

10 here because that has a very broad distribution 

11 in itself. It tended to broaden the model 

12 because alpha exposures have a raised 

13 effectiveness factor that go all the way up to 

14 100 on one end and two on the bottom end. So

15 anything that tends to increase the full 

16 distribution would minimize this difference.

17 It's hard to say. You know, we 

18 picked certain parameters we thought would tend 

19 to show a fair analysis, but I can guarantee you 

20 that we could run this different ways and come 
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1 up with different numbers. It just struck me 

2 that there were these huge differences with sort 

3 of a routine analysis.

4 This is sort of a run of the mill alpha 

5 exposure and this is where people tend to be 

6 compensated more often, as well, with alpha 

7 exposures to the lung. In fact, I guess I could 

8 argue that alpha exposures to many of these 

9 organs would not almost be realistic.

10 CHAIRMAN MELIUS: That's what I 

11 would say, yeah.

12 DR. NETON: And if you substituted 

13 something like photon exposures to get there, it 

14 would probably make these comparisons even 

15 broader. That would be my guess, because they 

16 don't have that alpha distribution on them.

17 But again, you know, as I point out 

18 several times, this was preliminary. We did 

19 this. It's food for thought.

20 MEMBER ROESSLER: Jim, were you just 
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1 asking what are the reasons that the ranking came 

2 up the way it did with the different types of 

3 cancers?

4 CHAIRMAN MELIUS: Yes.

5 MEMBER ROESSLER: Is there some 

6 rationale behind that, some reason that certain 

7 ones would come up really quite a bit higher than 

8 others?

9 CHAIRMAN MELIUS: And I think Jim's 

10 explanation is correct, that it has to do with 

11 sort of the nature of the distributions, 

12 differences between alpha and other exposures, 

13 and then also the distributions found within the 

14 models and so forth. So it's not, you know --

15 DR. NETON: The 84th percentile of a 

16 cancer model, or the 95th percentile of the 

17 cancer model versus the full distribution is 

18 different for each cancer model.

19 If you could say, and I think I 

20 pointed out, the 84th percentile is kind of a good 
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1 surrogate for the full distribution. The 

2 difference between a PC of the 84th to the 95th 

3 percentile is dependent on how broad that cancer 

4 model is. And some are known with more certainly 

5 than others.

6 I mean, but there's many, many, many 

7 factors in these models. You really can't 

8 characterize them actually as a distribution. 

9 They're more -- I call them histograms.

10 MR. BARTON: I have a question. I 

11 mean, this sort of assumes that we've identified 

12 which strata we want to take a look at and compare 

13 against the all-worker.

14 I mean, do you have any ideas or 

15 thoughts on how you would go about initially 

16 identifying that strata? I mean, you said, you 

17 know, construction workers or --

18 DR. NETON: Well, that gets back to 

19 the last discussion.

20 (Laughter.) 
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1 DR. NETON: I don't know. I mean, 

2 you know, how you do that? You have to pick 

3 something, and to be fair I think you got to be 

4 consistent.

5 But I don't know, I just don't know. 

6 I mean, some are easy. Some are obvious. You 

7 know, chemical operators, the guys that got their 

8 nose in the material and there's airborne. Some 

9 maybe are less obvious.

10 CHAIRMAN MELIUS: At one o'clock he 

11 was sure but we wore him out.

12 (Laughter.)

13 DR. NETON: Now I have no idea. 

14 Okay, so that's this paper. Again, this is 

15 preliminary work. You know, it's very 

16 interesting how it came out. It was a lot bigger 

17 difference than the 100 millirem, you know, where 

18 we said that there was no difference.

19 I think this is good food for thought 

20 and I'm not married to this analysis so any valid 
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1 criticisms are totally acceptable to me.

2 DR. MAKHIJANI: Definitely more 

3 interesting.

4 CHAIRMAN MELIUS: And you have three 

5 weeks. Jim’s allowed to have vacation again on 

6 this paper and --

7 DR. NETON: Okay, yes. Thank you.

8 DR. MAKHIJANI: Can we take vacation 

9 after that?

10 (Laughter.)

11 CHAIRMAN MELIUS: Oh, yeah. I think 

12 SC&A should give you a big bonus.

13 DR. NETON: All right, now I’m 

14 looking for the last paper.

15 CHAIRMAN MELIUS: This was Jim's 

16 birthday present to me.

17 DR. NETON: Was it? I didn't know 

18 that. Happy birthday.

19 CHAIRMAN MELIUS: I got OPOS too.

20 (Laughter.)
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1 MEMBER BEACH: So let the record note 

2 that your birthday's on June 16th, is that what 

3 you're saying?

4 CHAIRMAN MELIUS: You didn't have to 

5 let the record --

6 (Laughter.)

7 CHAIRMAN MELIUS: Though I have been 

8 told it's someplace on the internet.

9 DR. NETON: First, I want to show 

10 something that I think might help. Somewhere on 

11 here I have a presentation. Coworker slides 

12 Idaho, okay.

13 All right, I put some background 

14 information here because I feel it's important 

15 that we all talk about the same thing. And 

16 you've seen these slides before but I think it's 

17 important to emphasize -- not these things, 

18 although I could think about those forever too.

19 Okay, this is just a summary of how 

20 you do coworker model calculations. And these 
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1 little shaded boxes show possible areas where we 

2 can take data and we do something with them.

3 You can take the urine data and that 

4 will be, what, all the urine data and then develop 

5 50th and 84th percentile urine data and you could 

6 use what SC&A is now calling the pooled analysis, 

7 which is all the urine data, and just rank it up.

8 There's actually at least five ways 

9 I can think of that you could use the data. You 

10 could just rank up all the pooled data. You 

11 could take a simple mean of the data per worker, 

12 individual worker, what would be the maximum 

13 possible mean. Or you can do some sort of 

14 time-weighted average, whether you do a reverse 

15 or a forward analysis, make some differences 

16 depending on what the data look like. Or you 

17 could do some sort of a connect-the-dots 

18 analysis, which gives you a little better 

19 resolution. Or you could do a full-blown dose 

20 reconstruction like John was just talking about. 
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1 There's many different ways to do it.

2 In my mind, the simple pooled data 

3 analysis is the least scientifically valid 

4 because it's not modeling people, it’s modeling 

5 samples. And this is a coworker model, it models 

6 individual workers’ exposures.

7 And if you can agree that the 

8 full-blown dose reconstruction for everybody is 

9 the gold standard, then going backwards you end 

10 up with the first choice being, in my opinion, 

11 the least desirable.

12 And then we’ll fit 50th to 84th 

13 percentile intake rates. This is where it gets 

14 tricky. Okay, so here is the distribution it 

15 will generate for one year, right? This is a 

16 one-year distribution, whether this is OPOS, 

17 stratified, I mean, OPOS, some stratify, doesn't 

18 matter, time-weighted average, full dose 

19 reconstruction, just you get this distribution 

20 for one year.
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1 Now you're going to take that 

2 distribution, and if the slides cooperate, 

3 you're going to come up with -- you're going to 

4 do an intake calculation.

5 So each of these data points is one 

6 of those distributions, in this case by year. So 

7 you got about nine or ten years here of data. And 

8 you assume that on this 3,700th day this person 

9 started to breathe some chronic amount of 

10 material, and what is the best fit intake through 

11 these points to give you a coworker model for this 

12 little piece of the model?

13 Okay, so now I want to give you an 

14 example, a real example, from Savannah River. 

15 It was the coworker model, I think, for uranium 

16 from Savannah River, and this little blue 

17 highlight area is the model from 1991 to 2000. 

18 So this is that chronic intake piece where each 

19 of these blue dots represents one of those 

20 distributions, okay?
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1 So we fit that to come up with -- this 

2 is the 50th percentile coworker model. So you 

3 take the 50th percentile of all those individual 

4 distributions and fit it across and get this 

5 curve and you come up with an intake. That 

6 particular intake down here is 58 dpm per day.

7 DR. MAKHIJANI: 50th percentile of 

8 all the individual --

9 DR. NETON: Right.

10 DR. MAKHIJANI: Which?

11 DR. NETON: So each of these 

12 distributions, okay, you take the 50th 

13 percentile, which is geometric mean, and that's 

14 the excretion for that year.

15 DR. MAKHIJANI: Okay, all right.

16 DR. NETON: Then you take all these 

17 excretions for each year and plot. We don't plot 

18 them here. You plot them here and then you fit 

19 an intake curve through here.

20 So what would a person have to breathe 
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1 in every day in order to have this pattern for 

2 ten years? That's a chronic intake. And we're 

3 saying for this entire ten-year period this 

4 person is breathing in almost 59 dpm per day, 

5 every day for ten years. That's the 50th 

6 percentile of the coworker model. That's 

7 different than the 50th percentile of the 

8 individual.

9 Now we go further than that. We say 

10 what is the 84th percentile excretion? So you 

11 take the 84th percentile from that curve and you 

12 do the same analysis and you generate this curve.

13 Now you see the 84th percentile is 

14 141.1 dpm per day. From that calculation you can 

15 calculate the GSD of the intake itself, which in 

16 this case is 2.4.

17 Now, we've adopted in practice never 

18 to assign a GSD of less than three. So anything 

19 less than three is automatically made three if 

20 the GSD is less than two.
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1 So in this particular case then the 

2 95th percentile is this number times -- the 95th 

3 percentile of a GM of 58, a GSD of three, is 358 

4 dpm per day and that is what we will assign.

5 So this worker will receive, every 

6 day, 358 picocuries per day over a ten-year 

7 period when the 50th percentile was really 58.7. 

8 So this is, in my opinion, a generosity built into 

9 this coworker model, for entire ten years, based 

10 on that bioassay data.

11 So when we talk about inputting into 

12 IREP values, we're not talking about inputting 

13 this curve. This curve has nothing to do what 

14 goes in IREP. It's this analysis here that goes 

15 into IREP. Well, it’s actually the dose that is 

16 calculated from this analysis.

17 So I think that's important to keep 

18 in mind because just because you can have a 

19 difference in one of these dots of 20 percent, 

20 doesn't mean it invalidates this entire ten-year 
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1 period.

2 You'd have to have multiple years to 

3 make a significant difference in this ten-year 

4 regime. And, in practice, many of these regimes 

5 are multiple years. I mean, they're obviously 

6 more than one year. So I think that's important 

7 to understand. It's how they're built.

8 Okay, enough on that. I just want to 

9 make sure we're all talking about the same thing. 

10 Okay, now let me get to the other paper. Bear 

11 with me. Time-weighted, okay.

12 So, the last time we talked, we had 

13 proposed this maximum possible mean analysis 

14 which was -- here we go. We had proposed this 

15 maximum possible mean. We just essentially used 

16 the maximum possible mean, which was a mean value 

17 of all the values.

18 Well, since that time, we got to 

19 rethinking about whether that is really the 

20 approach we want to use, because in reality you 
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1 have to account for differences in excretion 

2 patterns. And the easiest way is just to show 

3 you one of these curves.

4 Daniel took the americium data for a 

5 certain time period. And you can see here, this 

6 is the average daily excretion for 1971 for some 

7 particular person. And all these red and blue 

8 dots are samples, whether they're censored or 

9 uncensored values.

10 But you can see, what you're really 

11 trying to do is get the area under the curve. How 

12 much did this person excrete in this particular 

13 year?

14 So, you know, you could integrate 

15 going forward, as we've done in our analysis, 

16 weighting each amount by the -- think I got --

17 yeah, each of these are little triangles. So you 

18 integrate these triangles where a guy is not 

19 excreting much and then he pops up. This is 

20 clearly an incident sample because he got a lot 
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1 of samples down, following down. You integrate 

2 all these little rectangles and you divide by the 

3 total days in the year and you get at the 

4 time-weighted OPOS.

5 Did he not list the -- yeah. The OPOS 

6 value for the maximum -- using the mean value was 

7 3.6. And in using this new time-weighted 

8 analysis you end up with 0.95, because clearly 

9 this is not contributing much to the overall 

10 excretion. It's a blip in time and goes down 

11 pretty quickly. 

12 Now, that's if you integrate forward. 

13 SC&A has suggested, and there's some basis for 

14 this, that you should integrate going backwards 

15 because the bioassay point is actually a measure 

16 of what happened before it, not after it.

17 What that tends to do, though, is it 

18 weights these incident samples. And that's why 

19 I like going forward, but I'm not married to 

20 either one.
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1 If you go backwards, then this 12 

2 would essentially generate a rectangle like 

3 this. It would go up from 0.3 to 12, so it would 

4 much more heavily weight the incident. The main 

5 difference is going backwards weights the 

6 incident samples a lot more.

7 MR. HINNEFELD: The assumption being 

8 that the incident sample would be taken shortly 

9 after the incident?

10 DR. NETON: Right. Which is, it's 

11 an incident, is typically what happens. 

12 So I don't want to get too much into 

13 the statistics. I mean, there are some formulas 

14 in here that give you how it's calculated. But 

15 in essence we're just saying we feel that a better 

16 approximation of a person's urinary excretion 

17 for the year is the time-weighted OPOS, which is 

18 based on this formula right here, whether it's 

19 M or M plus one. I'm not going to argue.

20 SC&A has corrections on that but, you 
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1 know, this value here, OPOS is the mean, the mean 

2 value, just linear mean, and this is the 

3 time-weighted value. We're now suggesting or 

4 recommending, hoping, that we will use this value 

5 in our calculations.

6 SC&A's analysis, as I read, still is 

7 suggesting that it okay -- you know, mean value 

8 is still appropriate, with some concessions, 

9 that if -- I'm not sure how you define this --

10 if there is significant data, what do you call 

11 it? Data --

12 MR. HINNEFELD: Data dominance.

13 DR. NETON: Dominance. Then it 

14 should be used. In my opinion, if it's okay to 

15 use when it's data dominant, why isn't it okay 

16 to use when you have three, four, five, six 

17 samples?

18 I think the argument where it says 

19 it's only one percent of the samples are affected 

20 by these type calculations. If you look at it, 
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1 though, it's the percentage of the samples, not 

2 the percentage of the people, you know, that are 

3 affected.

4 So you want to represent the people 

5 affected, that's very different than saying it's 

6 one percent of the samples. It's typically 

7 going to be much more than that.

8 So I feel, I strongly feel, that this 

9 is a more appropriate approach than using the 

10 pooled data. It may or may not be -- it's more 

11 accurate than just taking a linear value, I mean, 

12 a simple mean value, because it definitely 

13 accounts for the time-dependent distribution, 

14 which I think is more appropriate.

15 As far as I could tell, the only 

16 argument for staying with the pooled data is it 

17 produces higher means and standard deviations, 

18 but I'm not sure that's valid given the technical 

19 reasons for using, you know, a more time-weighted 

20 approach. So that's it in a nutshell. I’d be 
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1 happy to entertain any questions.

2 MR. BARTON: Well, if I may, I think 

3 that we all agreed over at SC&A, when we saw the 

4 time-weighting, that it represents kind of an 

5 improvement over unweighted OPOS because it has 

6 an element of time in it now.

7 But I think really our concern is just 

8 with the averaging in general. And it's not only, 

9 essentially, in the end product you end up with 

10 lower assigned doses than you would with the 

11 older pooled model.

12 It was our understanding in reviewing 

13 the literature that basically the scientific 

14 validity behind averaging was that the mean value 

15 of a worker's excretion rate over some time was 

16 proportional to their intake over that time.

17 But it is our understanding, and 

18 maybe you can react to this, this is one of our 

19 findings in RPRT-53, that when you take the mean 

20 of a worker's sample and it's okay to do that if 
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1 you're only following a single intake. And I 

2 guess that's where our major misgivings are with 

3 it. It's just that we don't have, over a given 

4 time period, you know, one acute intake.

5 It's a mixed bag. You're going to 

6 have periods of no exposure, periods of, you 

7 know, medium chronic exposure. Then you might 

8 have acute exposure thrown in there and we're 

9 averaging them all together, and I'm not sure if 

10 it maintains that scientific credibility in 

11 that.

12 If I might, specifically from NCRP 

13 Report 164, it says, and this was quoted in our 

14 report, "This appendix provides a summary of the 

15 least squares method formula that can be used to 

16 derive the intake starting from measurements of 

17 activity in bioassay.

18 "The formulas assume only one intake, 

19 no prior knowledge about the magnitude of the 

20 intake, biokinetic model parameters are known 
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1 perfectly, and all the measures are independent 

2 and properly normalized."

3 So I guess I'd like to hear, or we'd 

4 all like to hear, your reaction to that. Because 

5 it was our understanding that, yeah, when you 

6 take the mean value of a worker's excretion rate 

7 it is a very good measure if we're only talking 

8 about a single intake, which is really where we 

9 came out with our principle finding from RPRT-53, 

10 is that we think OPOS does have a place and it's 

11 after that -- if we go back to that chart you 

12 showed us -- after that spike acute intake and 

13 that, you know, averaging those values that are 

14 clearly a result of that intake, which would pose 

15 sort of the data dominance problem of that worker 

16 submitting more samples than your normally 

17 chronic exposed worker, that we felt that was 

18 really the place for OPOS, whether unweighted 

19 and, like we just said, we feel the 

20 time-weighting represents a significant 
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1 technical improvement. So, I mean, do you want 

2 to comment on that?

3 DR. NETON: Okay. Tom, are you 

4 still on the phone? Are you on mute? Tom 

5 LaBone?

6 MS. CHALMERS: Hey, Jim. This is 

7 Nancy. Tom had to leave.

8 DR. NETON: Oh, he had to leave? Oh, 

9 great, because this is a question that Tom -- we 

10 talked about this and I think that's a 

11 misinterpretation of the NCRP document where 

12 it’s single intake. 

13 DR. MAKHIJANI: Joyce is the one who 

14 -- Joyce, are you on the phone?

15 MR. KATZ: She may have had to leave.

16 DR. LIPSZTEIN: Hi.

17 DR. MAKHIJANI: Joyce, do you want to 

18 comment on that, since you did the original 

19 analysis, if I remember right.

20 DR. LIPSZTEIN: -- interpretation of 
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1 the NCRP document or the agency document. 

2 That's exactly what they wanted to say. 

3 Actually the agency document, I wrote that 

4 paragraph but I think that's why you came out with 

5 the time-weighted OPOS, right?

6 DR. NETON: Yeah.

7 DR. LIPSZTEIN: They thought that 

8 the OPOS, the mean was only proper for a single 

9 intake, that when you had other measurements that 

10 didn't relate to that intake that the OPOS 

11 couldn't be applied. I thought that that's why 

12 we came out with the new time-weighted OPOS 

13 approach.

14 We thought a lot about the 

15 time-weighted OPOS and we came out with the same 

16 thing that we had before, that if you had, if you 

17 want to compare two distributions and the 

18 distributions don't have the same monitoring 

19 protocol -- so it doesn't matter if you use 

20 time-weighted OPOS or not -- it's not appropriate 
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1 to compare them if they have different monitoring 

2 protocols.

3 But I think that's what you came out 

4 on the first discussion we had today on the first 

5 paper -- correct me if I’m wrong or if I 

6 misunderstood -- but on the first paper that we 

7 discussed today, when it was appropriate to do 

8 a coworker model, how to do coworker models and 

9 things like that.

10 I think one of the things that was 

11 agreed upon is that you have to look at the 

12 monitoring protocol to see if they are the same 

13 strata or not, right?

14 DR. NETON: Yeah, yeah, definitely. 

15 I agree with that. You know, there are going to 

16 be some incident samples embedded within a 

17 routine monitoring program. I mean, that's just 

18 going to happen.

19 And rather than just try to guess and 

20 strip out the incident samples, if we leave them 
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1 in there and we use time-weighted OPOS, it will 

2 accurately reflect the excretion pattern for the 

3 year. And, if anything, the incident sample is 

4 going to drive the value slightly higher, the 

5 time-weighted value, but not much.

6 As you can see, if you account for 

7 that little blip of an incident that happens over 

8 a week period, even though it's a fairly high 

9 value, it adds very little to the overall urinary 

10 excretion for the year.

11 DR. LIPSZTEIN: Yeah, I made some 

12 calculations and it makes -- using either a 

13 single incident and the continuous intake using 

14 the time-weighted OPOS, the way you put it, not 

15 the way that Harry has suggested. And you come 

16 out with an intake that is, if you use the 

17 continuous intake, if you come out with a total 

18 intake in the year which is about one half of the 

19 one if you use the acute intake.

20 DR. NETON: It depends, Joyce. It 
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1 depends on whether the adjacent sample, the 

2 previous sample, is lower or higher. And if you 

3 could do it both ways for a large number of cases 

4 I guarantee it's probably going to come out about 

5 the same on average.

6 I'm not against going backwards, you 

7 know, backwards integration. That doesn't 

8 bother me. I mean, I'm totally willing to accept 

9 that. That's a detail of implementation.

10 I just think that the time-weighted 

11 approach is a much more accurate depiction of the 

12 person's urinary excretion for the year, rather 

13 than treating them as individual samples, which 

14 makes no sense to me, to be honest. It just makes 

15 no technical sense.

16 I think an integration of the area 

17 under the curve, and even if you went to a 

18 connect-the-dots, a trapezoidal-type analysis, 

19 it's a little better even, and more accurate. 

20 But I think it gets closer and closer to the true 
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1 intake of the person than if you did a full-blown 

2 model on everybody, intake model.

3 So that's why we're recommending 

4 using time-weighted OPOS. And I'd be happy to 

5 implement a backwards integration. Wouldn't 

6 bother me.

7 MR. BARTON: Well, I think part of 

8 our concern was also that we thought that the 

9 science behind doing the averaging, which is 

10 going to get us closer to what the actual intake 

11 was, we had questioned whether it applies to 

12 situations when you have mixed intakes, 

13 essentially.

14 And it sounds like NIOSH feels that 

15 we may have misinterpreted that report. So, 

16 maybe it’s a good idea to hear, you know, 

17 officially from Tom and he can --

18 DR. NETON: Yeah. Yeah, we could 

19 ask Tom about it. That’s not a problem.

20 MR. BARTON: -- and that might 
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1 alleviate that concern.

2 MR. STIVER: This is John Stiver. I 

3 have a question for Joyce. Maybe for Harry too.

4 Now, in our February report on OPOS, 

5 we had gone through, I think it was in Section 

6 7.2 or one of those subsections there, that we 

7 had shown that, you know, this least squares 

8 weighting through the origin with the weightings 

9 that were, I believe, inversely proportional 

10 with variance could be shown to be mathematically 

11 related to a single intake, as according to the 

12 NCRP report.

13 And then the jumping-off point after 

14 that was, well, let's see how well OPOS does at 

15 estimating the true mean value of the excretion 

16 rate. And so the time-weighting then gets you 

17 a better approximation of the mean excretion 

18 rate, but it's still, in my mind, and maybe I'm 

19 wrong here, only applies to the single intake.

20 It doesn't necessarily -- it doesn't 
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1 provide more credibility for any other type of 

2 intake. I'd like to hear Joyce's response on 

3 that.

4 DR. LIPSZTEIN: Well, I think that 

5 the time-weighted is better than the OPOS itself, 

6 like it was the maximum, I don't know how it's 

7 called, the MPM.

8 MR. STIVER: Oh, the maximum 

9 possible mean.

10 DR. LIPSZTEIN: The one that was 

11 before, because when you had the excretion rates 

12 and you had -- for example, let's say the ones 

13 exactly like NIOSH is doing.

14 Suppose you have someone that didn't 

15 have an excretion or was not monitored during 

16 that period of time and suddenly he had an 

17 incident and assume the NIOSH proposed method you 

18 would, and if he didn't have any other monitoring 

19 the year before, you would apply that first 

20 monitoring result, which is the incident, to the 
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1 whole year before it.

2 So it's not that it's scientifically 

3 claimant-favorable but -- I'm sorry, that it's 

4 not scientifically correct but 

5 claimant-favorable, of course, because you are 

6 applying to the whole -- you know, let's say for 

7 one semester a guy didn't have any samples taken 

8 and then suddenly he has an incident and had one 

9 sample taken.

10 So the result of this sample would be 

11 applied for the whole six months that he didn't 

12 have any sample so this is claimant-favorable 

13 even if it's not, you know, scientifically 

14 reliable but it's claimant-favorable.

15 The problem with the OPOS as it was 

16 before is that it was not scientifically correct 

17 and was not claimant-favorable also.

18 So now we are dealing with that the 

19 formula could be either, as NIOSH pointed out, 

20 the way NIOSH is doing the time-weighted, or the 
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1 way Harry is proposing, which might be 

2 claimant-favorable.

3 But I don't think, neither of them is 

4 completely scientifically completely correct. 

5 But I think we are looking at something that is 

6 claimant-favorable.

7 My main complaint with this is that 

8 when you use this to compare to strata you have 

9 to be sure that the full strata, the full 

10 distributions, have the same monitoring 

11 protocol, otherwise you cannot compare them. 

12 But I think that this is explained on the first 

13 paper.

14 DR. NETON: Yeah, I agree with you, 

15 Joyce.

16 MR. BARTON: I guess the way I see it 

17 is we're kind of trying to weigh two things. 

18 One, I mean, rightly or wrongly, OPOS is sort of 

19 a data reduction technique.

20 Like you said, the end result is going to be a 
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1 little bit lower, which is okay if it's truly more 

2 scientifically defensible than the old method.

3 And that's sort of where we have to 

4 I guess come to a conclusion and put our heads 

5 together, and Tom can give us his interpretation 

6 of what our original finding was, was that it is 

7 scientifically defensible absolutely after a 

8 single intake, but maybe not if we're trying to 

9 cover multiple types of intakes, chronic, no 

10 exposed and acute, over the same averaging 

11 period. And I think that's kind of what we have 

12 to weigh.

13 If that is more scientifically 

14 defensible than the old method, well, then we can 

15 weigh that against the fact that the doses might 

16 be lower but we're actually getting closer to 

17 accurate dose reconstruction.

18 DR. NETON: I agree.

19 CHAIRMAN MELIUS: And the question 

20 is when is it accurate enough?
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1 MR. BARTON: Sure.

2 CHAIRMAN MELIUS: Because we're not 

3 going to have perfect accuracy with these 

4 circumstances.

5 And then, secondly, are there 

6 circumstances where it's not appropriate? I 

7 mean, we've already talked about the monitoring 

8 issue, but there may be other situations, in 

9 terms of the nature of the incidents or whatever, 

10 in the way the monitoring programs were done or 

11 whatever, that may, you know, may just not be 

12 appropriate to use it in those.

13 And I don't think we have to look for 

14 extreme examples but if there are some that are 

15 practical that we encounter, we, you know, ought 

16 to be aware of those.

17 DR. NETON: Yeah, I agree. The one 

18 we discussed earlier was if you have a purely 

19 incident-based sampling program, and you're 

20 going to use this technique, you can come up with 
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1 a maximum bounding value and then we're going to 

2 have to decide is that sufficiently accurate?

3 CHAIRMAN MELIUS: Yes.

4 DR. MAKHIJANI: Joyce or Bob, have we 

5 ever kind of made a table that compares the pluses 

6 and minuses, you know, here is the pooled data, 

7 here is the simple OPOS, here is the time? I'm 

8 kind of thinking that might give us a perspective 

9 because --

10 DR. NETON: Compare how, though?

11 DR. MAKHIJANI: You know, you've 

12 laid out some of the problems with the pooled 

13 model and you've laid out the problems with OPOS 

14 and the time-weighted OPOS and, you know, whether 

15 it's single intake and what do you do if there 

16 are multiple intakes and how close is it? 

17 Because ultimately you're trying to get 

18 something that is close to the -- that represents 

19 something close to the intakes. And maybe it's 

20 too simple-minded but --
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1 DR. NETON: Well, Arjun, like I say, 

2 the closer you come to a -- here's one of the cases 

3 where it used the connects-the-dots analysis. 

4 I'm just showing this on the screen. Rather than 

5 using rectangles, we're using trapezoids. And 

6 that's going to be a little closer. But the 

7 question is, how far do you go? Because the gold 

8 standard is to do a full-blown intake calculation 

9 for this to get the actual intake that the person 

10 experienced during that year. That's what we're 

11 really trying to get at.

12 The closer you approximate these dots 

13 under real conditions, the closer you're going 

14 to get, and I can guarantee you it's not just 

15 using all the data in a pooled analysis and 

16 fitting a distribution to it. This is closer. 

17 The next closer one is the gold standard, which 

18 is a full-blown dose reconstruction.

19 So, to me, if you buy into the fact 

20 that a full-blown dose reconstruction is the gold 
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1 standard then you work backwards, this example 

2 on the screen would be the second best, 

3 rectangles followed by mean values followed by 

4 -- I'm not even sure using all the values makes 

5 any sense at all. So, I don’t know.

6 DR. LIPSZTEIN: My thought, it's 

7 like if you have a person that was working the 

8 whole year in the facility, suppose you have 

9 someone that only worked for three months or six 

10 months at the facility in that one year, let's 

11 say in 1970. Then he worked in '71 and '72.

12 But let's say in 1970 he only worked 

13 six months on a certain facility and when you do 

14 the pooled data or when you do the pooled dose, 

15 if you have during that six months special 

16 working that made the intake and excretion rate 

17 of all of the workers during that six months go 

18 higher because you had a special job done there, 

19 and the first six months of the year you didn't 

20 have anything.
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1 But if there were some -- you want to 

2 calculate someone that was a construction 

3 worker, for example, and he came only on the last 

4 six months, you're not going to use the whole year 

5 for him. You are only going to use six months, 

6 right?

7 DR. NETON: Yeah. Well, that’s --

8 DR. LIPSZTEIN: But if they are using 

9 only six months and giving him the intake of a 

10 whole year where people had periods of no intake 

11 with periods of intake, then his intake is not 

12 going to be claimant-favorable. It will be the 

13 opposite.

14 DR. NETON: Right, but I think under 

15 the time-weighted approach it would be divided 

16 by the days of exposure, days he worked, right?

17 DR. LIPSZTEIN: Yeah, right. But 

18 that would give the mean exposure, the average 

19 exposure for the year, which is okay, the time 

20 average.
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1 But what you lose is, when you have 

2 this time-weighted or OPOS anyway, one of them, 

3 you lose, you know, the division like before when 

4 you had the pooled approach, you used to divide 

5 it by quarters, so you could see for any quarter 

6 of the year if you had more exposure than the 

7 others.

8 I agree with you that, in the mean, 

9 for a worker that's worked the whole year, if you 

10 use the time-weighted it's going to be more or 

11 less his intake.

12 But if you had someone that worked 

13 only on that period of time where you had the high 

14 exposure, then you are going to assign to him an 

15 exposure that is less than what he really got if 

16 you use the time-weighted for all the workers. 

17 I don't know how you are planning to deal with 

18 those cases. Do you understand me, Jim?

19 DR. NETON: I heard the first part. 

20 The first one you agreed with me and I kind of 
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1 went blank after that.

2 (Laughter.)

3 DR. LIPSZTEIN: Okay, okay. Let's 

4 say you had the pooled approach like before, 

5 okay? You divided excretion rates in quarters 

6 of the year, let's say 1970, okay, you had four 

7 quarters.

8 In one of the quarters you had a high 

9 exposure, and you could see that with the pooled 

10 data, and actually in some facilities you'll get 

11 a higher intake for that --

12 DR. NETON: Oh, okay. I think I 

13 understand what you're saying, Joyce. We're not 

14 obligated to use annual data. The OPOS examples 

15 we’ve provided are annual data because that's 

16 what we currently have.

17 But if we have quarterly data, we 

18 would use it. It would be the same kind of thing. 

19 It would be the quarterly OPOS, I mean, the 

20 quarterly time-weighted averages.
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1 So I don't know. You know, to answer 

2 your question, we would use all the data we can, 

3 all the data that are available, to the extent 

4 we can. And if we have enough data to do 

5 quarterly time-weighted averages, we would.

6 DR. LIPSZTEIN: Okay, but this is not 

7 explicit, right, and there are --

8 DR. NETON: I think when I talk about 

9 other monitoring intervals --

10 (Simultaneous speaking.)

11 DR. NETON: Yeah. No, we're not 

12 obligated to use a yearly basis. Maybe that 

13 seems that way because that's all the examples 

14 we've had. But I think I tried to put in there 

15 yearly or other monitoring interval, implying 

16 that it could either be more than one year or less 

17 than one year.

18 I could make it more explicit, 

19 because if we have quarterly data -- and you're 

20 right, early on in the uranium measurements at 
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1 some of the uranium plants we have enough where 

2 we currently have quarterly data and we wouldn't 

3 convert that to an annual OPOS. It would be a 

4 quarterly OPOS, quarterly time-weighted OPOS.

5 DR. LIPSZTEIN: Okay. And another 

6 thing, before when OPOS was derived, you were 

7 using all sensory data as you go to the minimum 

8 detectable activity, but when you gave the 

9 example of the time-weighted OPOS, you didn't do 

10 that.

11 DR. NETON: Right.

12 DR. LIPSZTEIN: Are you going to do 

13 it or not?

14 DR. NETON: Well, you don't have to 

15 take averages unless you have multiple samples 

16 in one day, right? So averages aren't involved 

17 anymore. And I am struggling with the idea of what 

18 to do with individual values that are negative. 

19 The scientist in me says that those are valid 

20 numbers. On a practical basis, I don't think we 
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1 would use them in the OPOS calculations.

2 MR. HINNEFELD: In a time-weighted 

3 OPOS.

4 DR. NETON: Time-weighted OPOS. 

5 Because there's just a number of issues. Using 

6 negative values is appropriate, in my opinion, 

7 when you're averaging values that are taken from 

8 the same distribution.

9 And in this particular case, these 

10 are taken from multiple samples over time under 

11 different exposure conditions. And I think that 

12 to be claimant-favorable we would just use at 

13 least -- I'm not sure whether we use a zero or 

14 the censored data point. I'm not sure. But I 

15 don't think I would end up using -- we would end 

16 up using negative values in time-weighted OPOS.

17 CHAIRMAN MELIUS: Okay, good. 

18 Thank you. Okay, so why don't we wrap up. It's 

19 close to 5:00. Any last words?

20 DR. NETON: I have no last words.
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1 CHAIRMAN MELIUS: Okay. So --

2 DR. NETON: Although everybody get 

3 me your material to work with.

4 CHAIRMAN MELIUS: Three weeks.

5 DR. NETON: And if it comes in two 

6 weeks and six days, it's going to take me a little 

7 longer to digest all the material but please feel 

8 free to comment early and often.

9 [Identifying Information Redacted]

10 MR. KATZ: All right, so we're 

11 talking about anyway September. Get your 

12 comments in by September.

13 CHAIRMAN MELIUS: Three weeks. 

14 Three weeks is simple. Three weeks from today.

15 MR. HINNEFELD: Three weeks from today 

is the 18th 16 of August.

17 CHAIRMAN MELIUS: Do you want to do 

18 the time-weighted average? The mean date of 

19 August?

20 (Laughter.)
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1 DR. NETON: I recall we have a 

2 Procedures Subcommittee coming up at the end of 

3 August.

4 CHAIRMAN MELIUS: August 18th.

5 MR. KATZ: Okay. Yes, I was 

6 thinking about SC&A when I was pitching for 

7 September, but okay.

8 CHAIRMAN MELIUS: If you need 

9 longer, you know --

10 DR. NETON: Yeah, these don't have to 

11 be formal --

12 MEMBER BEACH: Just send a comment to 

13 that effect.

14 MR. STIVER: It would probably be 

15 better to try to pull them together soon.

16 DR. NETON: Yeah, I mean, they don't 

17 have to be formally, you know, written up in fancy 

18 White Papers or anything. Just --

19 CHAIRMAN MELIUS: And everyone's 

20 been so good that I think you can all come back 
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1 here tomorrow for the meeting.

2 (Simultaneous speaking.)

3 CHAIRMAN MELIUS: And thank you, 

4 everybody. Thanks, Paul, everybody on the 

5 phone.

6 MR. KATZ: Yes, thanks for hanging in 

7 there. I know it's tough. So take care, 

8 everyone on the phone. Good night.

9 (Whereupon, the above-entitled 

10 matter went off the record at 4:55 p.m.)

11

12

13

14

15

16

17

18

19

20
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1