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1 

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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

+ + + + +

TUESDAY

MARCH 10, 2015

+ + + + +

The Work Group convened by 

teleconference, at 1:00 p.m. Eastern 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

TERRIE BARRIE

BOB BARTON, SC&A

LIZ BRACKETT, ORAU Team

NANCY CHALMERS, ORAU Team

HARRY CHMELYNSKI, SC&A

JOE FITZGERALD, SC&A

TOM LABONE, ORAU Team

JENNY LIN, HHS

JOYCE LIPSZTEIN, SC&A

ARJUN MAKHIJANI, SC&A

DAN MCKEEL

JOHN MAURO, SC&A

JIM NETON, DCAS

DANIEL STANCESCU, DCAS

JOHN STIVER, SC&A

TIM TAULBEE, DCAS
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1 P-R-O-C-E-E-D-I-N-G-S 

2 (1:01 p.m.)

3 MR. KATZ: Welcome, everyone. This is 

4 the Advisory Board on Radiation and Worker Health, 

5 the SEC Issues Work Group. 

6 For our meeting today, we're talking 

7 about coworker modeling implementation 

8 guidelines, and we also are, because it's related 

9 to that sort of by example talking about one of the 

10 coworker models for the Savannah River Site. So, 

11 when I go through roll call for all Agency-related 

12 personnel, please also speak to conflict of 

13 interest when we do that. So, let's get started with 

14 roll call starting with the Board Members, please.

15 (Roll call.)

16 MR. KATZ: All right. So, the agenda is 

17 on the NIOSH website, as well as the two documents 

18 related to the agenda today. It's guidelines, draft 

19 guidelines for coworker modeling, and also 

20 Savannah River Site -- a couple of documents 
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1 related to Savannah River Site. And, Jim, it's your 

2 meeting.

3 CHAIRMAN MELIUS: Okay. Thank you, Ted, 

4 and thanks, everybody for joining us. 

5 We're going to separate the meeting 

6 into two parts. The first part we'll talk about the 

7 --- it's called Revision 4 of the Draft Criteria 

8 for the Evaluation and Use of Coworker Data Sets. 

9 And there's a document that Jim Neton and others 

10 have put together, but it came out of a number of 

11 discussions within this Work Group, and with this 

12 Work Group, and NIOSH, and SC&A about how best to 

13 approach the evaluation of coworker data sets. So, 

14 I believe probably the easiest way we've dealt with 

15 these before is if, Jim Neton, you want to walk us 

16 through sort of what the updates are. We don't have 

17 to go through every detail but sort of generally 

18 where you've made changes since the last time, and 

19 I think that'll open it up for discussion.

20 DR. NETON: Okay, Dr. Melius, thanks. 
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1 Can everybody see the document on their monitors, 

2 or no?

3 MEMBER BEACH: I have the document in 

4 paper form. Do we have to see it on the monitor?

5 DR. NETON: Well, if anybody's on Live 

6 Meeting can they see it?

7 MR. KATZ: Yes, it's there, Jim.

8 DR. NETON: Okay, that's all I want to 

9 know. I couldn't tell if it got up there. My 

10 computer went wild and started making like multiple 

11 iterations of the same file.

12 MR. KATZ: Right, I saw that, but it's 

13 there. It's fine right now.

14 DR. NETON: Okay. So, those are on Live 

15 Meeting. I want to just kind of use it as a 

16 background template for us to speak from, but 

17 everyone should have a copy of Revision 4 that was 

18 issued February 26, 2015.

19 I went back and reviewed the 

20 transcripts of the last Board meeting where we 
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1 presented Revision 3 and didn't see a lot - didn’t 

2 receive a lot of comments on that document. 

3 Actually, I don't think I received any from the 

4 Advisory Board. I got a few internal NIOSH 

5 comments, so I took it and revised this from Rev. 

6 3 which was issued October 30th, 2014.

7 Not a ton has changed here. I did move 

8 some things around and added a few new pieces, 

9 though. Mostly, the first thing I did was I moved 

10 Section 2.3, the old Section 2.3 which was titled, 

11 bear with me, "Applicability of Monitoring Data to 

12 Unmonitored Workers." I moved it to Section 3.1 

13 because I felt that it fit better there, and I 

14 thought it improved the readability, so that just 

15 in total moved over there. And then I added some 

16 language to Section 3 at the beginning of --- where 

17 did I do that? At the very beginning of Section 3 

18 there's some new language, some introductory 

19 language. It talks about the finalized coworker 

20 data sets and sort of a configuration control 
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1 section. 

2 We had some internal discussions within 

3 NIOSH and felt that this is a pretty important 

4 aspect. It's sort of what you see in epidemiologic 

5 research where they would call it cleaning the data 

6 set or something to that effect where you go through 

7 the data set, especially if it's electronic, and 

8 start looking at outliers and things that don't 

9 belong there, like maybe chelated samples. You need 

10 to do that, but we also need to be very systematic 

11 in how we control those various sets so that we can 

12 go back in time later and at least figure out what 

13 we did to get where we are. I think it's a very 

14 important piece of that, so that whole first 

15 introductory section was added there. I don't know 

16 if you've had a chance to read it. Not very long, 

17 but I think it's important.

18 And, finally, in Section 4, I debated 

19 a bit about this. I indicated the last time we met 

20 that I need to think about this evaluation of 
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1 stratification and what we're going to do about 

2 data analysis. 

3 The statistics -- I think in looking at 

4 this document, I went back and looked at the 

5 original review of ORAU Report 53 which is how to 

6 analyze for stratification, and SC&A came back and 

7 had five findings -- or eight findings. And I think 

8 in this document now I've addressed --- we've 

9 addressed at least five of those findings to some 

10 extent. Not that we've addressed them, but we've 

11 formally put the criteria in here that they 

12 believed were lacking in the analysis of 

13 stratification, such as the data completeness and 

14 such. The three remaining findings that have not 

15 been addressed relate to the statistical analysis. 

16 Now, I think that this document itself 

17 sort of walks you through a qualitative analysis 

18 of a data set such that you really end up 

19 stratifying in a lot of locations where we might 

20 not have previously. For example, when it talks 
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1 about stratification of incident versus routine. 

2 That's sort of a --- that is a default 

3 recommendation in the document. So, as you go 

4 through this you end up stratifying for various 

5 reasons other than doing a statistical test. 

6 I do believe, though, at some point 

7 we're going to get down to the situation where we 

8 have a remaining set that, for instance, is all 

9 routine data that we still believe that 

10 stratification could be necessary. An example may 

11 be reactor operators at Savannah River who were 

12 exposed to tritium versus others. And I've outlined 

13 in this section now the three criteria where we may 

14 --- the three evaluation criteria where we may end 

15 up requiring some test of stratification.

16 There's no way around doing some sort 

17 of statistical tests. I didn't want this document 

18 to be the holdup in moving forward with the good 

19 stuff that's preceded in here, so I took and made 

20 the statistical evaluation criteria a little less 
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1 prescriptive. I said that it needed to be done, but 

2 I didn't include in here the exact statistical test 

3 that needs to be done to demonstrate that these data 

4 sets should be stratified, so that's where I ended 

5 up with this document. That in a nutshell is where 

6 we are at this point.

7 CHAIRMAN MELIUS: Very good, Jim. And, 

8 actually, I think I --- I more than think. I do 

9 agree with your approach. I'm worried about this 

10 becoming overly prescriptive because I don't think 

11 that would be appropriate given the, sort of, wide 

12 range of situations which we encounter at different 

13 sites and so forth. And I think it's very hard for 

14 us without specific example, or a set of specific 

15 examples to really under --- you know, we don't 

16 want to be overly prescriptive because I think we 

17 have to look at the situations. And I think, again, 

18 like we did with our SEC evaluation documents, I 

19 think what we're trying to put forward is sort of 

20 what's the general approach, and then what should 
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1 be considered in developing and evaluating 

2 coworker models, and then those should be addressed 

3 in whatever report would come from NIOSH, and then 

4 we can evaluate it on a sort of a case by case basis. 

5 So, I think the approach you've taken is the best 

6 approach.

7 DR. NETON: Thank you.

8 CHAIRMAN MELIUS: And I agree, some of 

9 the statistical considerations, I almost would 

10 think that, you know, it might be best dealing with 

11 them on an individual basis. You know, is this 

12 --- or something meets the other criteria and 

13 you're coming down to where you have to make a 

14 statistical analysis as to whether or not the 

15 stratification of a coworker model is appropriate. 

16 Then we would, you know, address that, you know, 

17 as that specific example. I think it's going to be 

18 hard to generalize on that because there are just 

19 so many different situations that might change our 

20 evaluation of that statistical analysis.
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1 DR. NETON: Yes, I agree. I think the 

2 first three sections are going to sort of triage 

3 out the easier ones that don't --- we will stratify 

4 without even the requirement for a statistical 

5 test.

6 CHAIRMAN MELIUS: Yes.

7 DR. NETON: You know, such as the 

8 incident-driven construction trades monitors, 

9 ones that are monitored that way versus the overall 

10 routine monitored workers. I think it will address 

11 a lot of the issues that we've had, but it still 

12 does allow for us to get down to the statistical 

13 analysis at the very end of the day, if that needs 

14 to be done.

15 CHAIRMAN MELIUS: Yes, and my favorite 

16 example is the --- a little bit different than 

17 yours, but it's the one of where there's only a few 

18 missing data points, a very small percentage are 

19 missing. You're sort of using a coworker model to 

20 fill those in, then I'm not sure that we need to 
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1 worry a lot about the statistical force of that; 

2 whereas, if it's a huge gap that we're trying to 

3 fill where there's been no data, or very little data 

4 collected, then I think we have to give a lot more 

5 scrutiny to what we're doing because statistically 

6 that's a lot more difficult to do, or may be a lot 

7 more difficult to do. It depends on lots of other 

8 factors there. But I think we would get that from 

9 the beginning of this, so the one through three. 

10 MEMBER ROESSLER: Jim, this is Gen. I'd 

11 like to make an overall comment on the paper. I read 

12 it in its revised form, and probably from my 

13 perspective as an editor, or maybe a has-been 

14 editor, I'm always looking for things that have 

15 scientific value, and this is so well-written, it's 

16 so precise, and it outlines things I think so well 

17 with regard to using coworker data.

18 You know, people can say well, you 

19 really can't do it, or you can do it, but this goes 

20 through and it outlines the situations where it 
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1 could work, and how it can be accomplished. And I'm 

2 just wondering if it would have --- could be 

3 published somewhere in the broader literature. It 

4 would seem there may be other places where this 

5 information really would be useful. 

6 DR. NETON: I appreciate those 

7 comments, Gen. It's certainly within the realm of 

8 possibility that we could do something like that. 

9 I hadn't really thought about that. You know, there 

10 aren't many programs that do what we do. This is 

11 a very sort of specific little niche business that 

12 we're doing, but let's think about it.

13 CHAIRMAN MELIUS: Yes. This is Jim 

14 Melius, again. I was going to say there's probably 

15 a fair amount written in the epidemiological 

16 literature, the occupational epidemiology where 

17 people are, you know, developing exposure models 

18 and so forth. But really the criteria there are 

19 different than in a compensation program.

20 MEMBER ROESSLER: There may be other 
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1 compensation --- I mean, there are other 

2 compensation programs --- 

3 CHAIRMAN MELIUS: Yes.

4 MEMBER ROESSLER: --- so that this 

5 might be general enough that it could be used. This 

6 is --- it seems NIOSH is putting so much in with 

7 the review by SC&A, and the Board, and so on. 

8 There's so much effort going into this, and when 

9 you come up with something that to me is so 

10 well-written and has so many important points in 

11 it, it's a shame to just have it buried somewhere.

12 CHAIRMAN MELIUS: Yes. No, I agree with 

13 you, Gen. Other Board Member comments?

14 MEMBER ZIEMER: This is Ziemer. I'll 

15 make a general comment. Again, I concur with Jim 

16 Neton's approach on this, particularly on Section 

17 4. I think that's where you've got to land. This 

18 is a criteria document where you lay out kind of 

19 a roadmap and the individual statistical analyses 

20 needs to be very site-specific in almost every 
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1 case, so I think this is the right approach. I also 

2 would concur with Gen's comment, but probably not 

3 a decision this Work Group can make on whether to 

4 publish this. But, anyway, I'm very comfortable 

5 with what the NIOSH approach here is on this 

6 particular document.

7 CHAIRMAN MELIUS: Well, I don't know, 

8 Paul, we're always giving Jim Neton lots of work 

9 to do, so ---

10 DR. NETON: Yes.

11 MEMBER ROESSLER: It's just a 

12 suggestion.

13 CHAIRMAN MELIUS: No, no, it's good. It 

14 was a good suggestion. 

15 MEMBER BEACH: This is Josie. I agree 

16 with all the comments. I thought it was 

17 well-written, and it explained a lot for my 

18 benefit.

19 CHAIRMAN MELIUS: Okay, thank you. 

20 SC&A, you have so many people on the line, I don't 
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1 know where to start.

2 MR. BARTON: This is Bob Barton. It's 

3 not so much a comment, but I guess just a clarifying 

4 question. During previous discussions on the 

5 stratification issue, notably the meeting we had, 

6 teleconference last October, it seemed like, you 

7 know, based on the transcript and what I 

8 recollected from that meeting that the approach is 

9 almost --- it's almost sort of a qualitative 

10 approach to whether you stratify. And I think the 

11 discussion sort of went something along the lines 

12 of if you have a legitimate reason to believe that 

13 you have two groups of workers that are different, 

14 and you have enough samples to build two 

15 distributions, and it meets all the other criteria 

16 that's spelled out in Sections 1-3 of the 

17 Implementation Guide, then just go ahead and build 

18 two coworker models, and see where the chips fall. 

19 And then you still have the statistical 

20 comparisons for situations where maybe it's not as 
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1 clear, or you have a population of workers who are 

2 all routine, for example, where you would apply the 

3 statistics. But when you had situations such that, 

4 you know, for example, I think you mentioned 

5 earlier on this call, you know, a routine monitored 

6 population versus an incident-driven, that you 

7 wouldn't even need to apply the statistics, 

8 necessarily. If you have a legitimate reason to 

9 believe these two groups are different, just go 

10 ahead and build the two coworker models. At least 

11 that was my impression. So I guess, one, is that 

12 a correct impression? And, two, is it still NIOSH's 

13 sort of feeling on the matter?

14 DR. NETON: Bob, I think you've hit it 

15 just right. I believe somewhere in here I mention 

16 that --- in the applicability section --- it's 

17 certainly our --- oh, yes, here. The second full 

18 paragraph on page 9 talks about different sites who 

19 have been monitored for different purposes. It 

20 talks about construction trades workers who were 
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1 intermittently monitored or only when an incident 

2 occurred, while those employees involved in 

3 routine operations would have been routinely 

4 monitored.

5 In this case, it would not be 

6 appropriate to combine the monitoring data for 

7 these two groups into a single coworker model that 

8 assumes a chronic exposure pattern; rather, the 

9 default in this case should be to consider separate 

10 coworker models. So, that's definitely prescribed 

11 in here pretty clearly, I think. 

12 The idea is that, you know, if we go down 

13 this sort of criteria, one, two, three, and then 

14 you end up and you say okay, I'm at step four, and 

15 I still have what looks to be a routinely monitored 

16 workforce, and I have a very wide range of job 

17 descriptions, I tried to spell out what needed to 

18 be in place to consider stratification, and that's 

19 the first three items in here in Section 4 where 

20 it talks about first, you have to have accurate job 
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1 categories and descriptions for all those workers 

2 that make up the data set. There also needs to be 

3 some reason to believe that, you know, there's a 

4 high-end exposure. And then, third, I put in here 

5 that we also have to have some knowledge that there 

6 were unmonitored workers in the higher-end job 

7 categories, or in this job category. 

8 For instance, it's possible that for a 

9 small set of workers that you have a coworker model, 

10 all the workers were monitored. Maybe not routinely 

11 that way, but it's possible. So, those are the three 

12 criteria. And if those three things are fulfilled, 

13 then you would consider doing some sort of 

14 statistical analysis, but that would be at the very 

15 end of the process.

16 I think what's going to happen, as I 

17 mentioned earlier, is we're going to end up having 

18 separate models for some workers now that we didn't 

19 previously, because you have to evaluate the types 

20 of the monitoring programs themselves. That 
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1 typically, at least in my mind, has been our major 

2 issue. We're comparing apples to oranges, and this 

3 document forces you to compare apples to apples.

4 I don't know if I answered your 

5 question. I think I did. 

6 MR. BARTON: No, you did. I mean, again, 

7 that was sort of a clarifying question to make sure 

8 that at least I was understanding where we were on 

9 that. 

10 I guess a follow on, if you had an 

11 incident-based monitored population, I mean, maybe 

12 this is getting beyond the scope of discussions 

13 today, but if you did have --- decided that we have 

14 an incident-based population, I mean, would the 

15 actual coworker model itself for that 

16 sub-population we'll call them, wouldn't it have 

17 to change since you're not longer in a situation

18 where you can safely assume chronic exposures over 

19 some intake regime time period? I mean, I'm just 

20 --- I'm trying to think this through. If you have 
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1 --- 

2 DR. NETON: Yes. That's going to have to 

3 be an implementation issue, but I do agree with you 

4 that needs to be considered. A chronic exposure 

5 model might not necessarily be sufficiently 

6 accurate. It might be bounding in that situation, 

7 but it needs to be considered.

8 Yes, it's not in here, but I think that

9 would have to be taken into consideration on a case 

10 by case basis. I just don't know any other way to 

11 do it. You have --- you know, if you can ---I think 

12 there's some verbiage in here that talks about if 

13 you have a very well-controlled monitoring 

14 program, a very well-defined project, for example, 

15 with some very good monitoring, you know, whether 

16 it's good air sampling, good alpha CAMs, 

17 contamination control, and you can demonstrate 

18 that all the upset conditions would have been 

19 detected, you know, maybe you don't need a coworker 

20 model in that situation, even though there may have 
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1 been an incident. But it's certainly --- all the 

2 incidents would have been detected, that sort of 

3 thing, so there's a number of different variations 

4 on this theme, I think.

5 MR. BARTON: I was going to say, sort of 

6 the corollary to that is like where there were 

7 relatively few incidents that occur. We may know 

8 there's some but, you know, is it really going to, 

9 you know, matter? We know that those weren't 

10 extremely high incidents. I mean, I think you sort 

11 of got that covered in your criteria but ---

12 DR. NETON: Right.

13 MR. BARTON: --- it is going to be ---

14 DR. NETON: And you also have a 

15 situation where you'll have incident-driven 

16 bioassay on a project that's short duration, but 

17 then you may have closeout bioassay samples on 

18 everyone, which adds another dimension to it.

19 MR. BARTON: Yes.

20 DR. NETON: But the main message is that 
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1 that should not be lumped in with the routine 

2 monitored population.

3 MR. BARTON: Right, thank you. I 

4 completely agree with that. 

5 CHAIRMAN MELIUS: Other SC&A comments?

6 DR. LIPSZTEIN: Yes, may I?

7 CHAIRMAN MELIUS: Sure.

8 DR. LIPSZTEIN: This is Joyce. Can you 

9 hear me?

10 CHAIRMAN MELIUS: Yes, we can.

11 DR. LIPSZTEIN: Okay. I'm going to 

12 repeat what's already been said, that this is an 

13 extremely good document. We appreciate it a lot. 

14 It touches in many problems that we had discussed 

15 before, and now we have it written in a very good 

16 way, and attempts many of the things that I've seen 

17 SC&A had been observed and discussed all along, so 

18 it's a very, very good document.

19 Just one point that I would like to 

20 --- I don't know if it could make it more clear, 
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1 is when you talked about the missed dose and results 

2 below the lower limit of detection, the limit of 

3 detection. There is just one paragraph talking 

4 about the missed dose, and I don't know if I 

5 understood it well. But if I understood it well, 

6 Jim, is on page 5, the final paragraph in 2.1. 

7 "Finally, the amount of dose that could have been 

8 received but not detected by a routine monitoring 

9 program must be evaluated to determine the 

10 magnitude of this missed dose is within the 

11 plausible bounds of exposure received by the 

12 workers."

13 Does this mean that when you have 

14 results below the limit of detection, we are going 

15 --- what is going to --- the standard would be to 

16 apply the limit of detection and not results lower 

17 than the limit of detection?

18 DR. NETON: I think that will take us 

19 probably into our next discussion on the trivalent 

20 actinide analysis, Report 55. But the idea that I 
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1 had here was that if, for instance, you only had 

2 the MDA reported for all the measurements, and it 

3 turned out that, you know, the missed dose was so 

4 large --- and I had in mind here a thorium intake 

5 which, as you know, has very poor ability to detect 

6 intakes for various reasons, but if the missed dose 

7 --- if you tried to just build a coworker 

8 monitoring based purely on missed dose and it ended 

9 up --- and the samples were very far apart, and you 

10 ended up with very high implausible doses, it's not 

11 --- it would not be considered sufficiently 

12 accurate. That's what I was trying to say.

13 I did not intend, though, to say that 

14 we would not use data below the MDA in the analysis 

15 of coworker models, and particularly in the OPOS 

16 analysis. And that --- I purposely didn't mention 

17 that in here. I think that's an implementation 

18 issue, and I think we can discuss that maybe at the 

19 second part of this discussion. That's why I really 

20 wanted to discuss that today, because in my mind 
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1 that is the only outstanding issue related to the 

2 use of the OPOS technique.

3 DR. LIPSZTEIN: Okay.

4 DR. NETON: That's where we're at.

5 DR. LIPSZTEIN: Okay, thank you.

6 CHAIRMAN MELIUS: Any other comments 

7 from SC&A? Okay, hearing none, any further comments 

8 from Board Members or reactions?

9 MEMBER ROESSLER: This is Gen. I have 

10 one comment on Section 4. This is very short and 

11 you jump into it very quickly. I think it would help 

12 --- this is editorial, I'm sorry, but in that first 

13 sentence if the last part were brought up to the 

14 beginning to say "the distribution of," so on and 

15 so forth, and then list the situations, it would 

16 be easier to understand. 

17 DR. NETON: I'm sorry, Joyce, I'm not 

18 following.

19 CHAIRMAN MELIUS: Gen.

20 DR. NETON: Gen. Where ---
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1 MEMBER ROESSLER: I can just send that 

2 to you. It's just a rewording that I think would 

3 help. 

4 DR. NETON: The first sentence for 

5 situation where accurate job categories obtained 

6 for all workers ---

7 PARTICIPANT: Move the distribution of 

8 potentially highly to the front part of the 

9 sentence and then say --- give examples, make it 

10 clear.

11 DR. NETON: Oh, yes, sure, that could be 

12 a rephrasing of that sentence.

13 CHAIRMAN MELIUS: Okay.

14 DR. NETON: Yes, we can do that. 

15 CHAIRMAN MELIUS: Open for discussion, 

16 obviously, but what I would like to do is suggest 

17 next steps for this document is that we get it to 

18 the Board Members, the Full Board, and that we 

19 --Jim, if you can present it at the Board meeting, 

20 hopefully, we'll get continued engagement on that. 
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1 And I think once we tell them it's almost final, 

2 that if anybody has any residual concerns or 

3 questions they will come up. And we, obviously, 

4 already put it on the agenda, so --- 

5 DR. NETON: That sounds good to me. I can 

6 distribute this to the Full Board once I --- I'll 

7 make this one change the Gen recommended because 

8 I do agree with it. I think I'm still going to leave 

9 it at Rev 4. I don't want to make a separate revision 

10 for a change in a sentence, but I'd be happy to 

11 present it. And I'm hoping that we're nearing this, 

12 because we're anxious on our end to start to try 

13 to implement this.

14 CHAIRMAN MELIUS: Okay, and that was my 

15 next suggestion, and you may have to give this some 

16 thought as to the example, but I think it would be 

17 helpful to this Work Group, our SEC Work Group here 

18 which is, you know, sort of work through this 

19 methodology with you, and guidance with you, that 

20 we then take and go through an example that would, 
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1 you know, sort of follow these guidance in terms 

2 of addressing these particular issues. 

3 DR. NETON: Yes, we can do that.

4 CHAIRMAN MELIUS: And that would be the 

5 step to implementation. I mean, so the --- I think 

6 it's ready for implementation, in general. I think 

7 --- what I'm concerned about is that we make sure 

8 that we --- that as you're implementing it, that 

9 you --- that we, sort of, reached agreement on what 

10 would be the appropriate ways of, you know, 

11 outlining this in a document, that when you first 

12 present, you know, a coworker model, you would walk 

13 through these steps.

14 DR. NETON: Yes.

15 CHAIRMAN MELIUS: In terms of what had 

16 been considered, and in terms of presenting 

17 information. I think that would be --- I think 

18 that's helpful in terms of ensuring, you know, sort 

19 of consistency, and making sure that there's no 

20 --- you know, you're not, sort of, sent back to the 
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1 drawing board, or something is missed that people 

2 are expecting to see, or to understand better, and 

3 you have to go back through and revise the report, 

4 and so forth. And I think we could do that in a 

5 --- hopefully, in a timely way, which is why I say, 

6 you know, let you sort of think about what would 

7 be a good example to start with. 

8 DR. NETON: Yes. I was just talking 

9 offline with Tim here a little bit. I think Savannah 

10 River is the obvious choice to start. I mean, 

11 there's other sites but we're ---

12 CHAIRMAN MELIUS: Yes.

13 DR. NETON: --- so far along with this 

14 site. We certainly won't have this by the next Board 

15 meeting.

16 CHAIRMAN MELIUS: Well, no, I'm not 

17 expecting it by the Board meeting. And I would do 

18 it, I think, as a --- I think we can do it as a Work 

19 Group effort.

20 DR. NETON: Yes.
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1 CHAIRMAN MELIUS: I don't know, it might 

2 take longer, but if there's something that would 

3 be between now and our follow-up Board meeting.

4 DR. NETON: Yes, I don't think the whole 

5 thing could be done, but we could certainly start 

6 with the pieces and parts of the data completeness, 

7 that sort of thing. And maybe that's the best way 

8 to approach it, is one step at a time. 

9 CHAIRMAN MELIUS: Yes, that's a good 

10 idea. 

11 DR. NETON: Sort through it logically. 

12 CHAIRMAN MELIUS: Yes. And I'm not sure 

13 we have to go through the whole --- all the steps, 

14 but ---

15 DR. NETON: Yes, I totally agree. I

16 mean, to me it's sort of like, you know, any new 

17 law that's written, no one knows what it means until 

18 you try to implement it, and follow it ---

19 CHAIRMAN MELIUS: Yes.

20 DR. NETON: --- and it gets tested in 
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1 the courts, so to speak.

2 CHAIRMAN MELIUS: Yes. I also think it 

3 would then --- you know, the extent that you have 

4 to go back and re-look at previous coworker models, 

5 it would sort of clarify the steps for that.

6 DR. NETON: Yes.

7 CHAIRMAN MELIUS: And we may come up 

8 with some other guidance or criteria that would 

9 come out of that. Again, I think you've covered 

10 everything well in a general fashion, but you, sort 

11 of, never know until you encounter it.

12 DR. NETON: Yes, and maybe once we do it, 

13 then we --- you know, sort of proceduralization can 

14 happen a little --- 

15 CHAIRMAN MELIUS: Yes.

16 DR. NETON: That sort of thing. 

17 CHAIRMAN MELIUS: Other Board Members 

18 think that would be helpful?

19 MEMBER ROESSLER: Yes, I do.

20 MEMBER ZIEMER: Yes, that makes sense to 
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1 me. This is Ziemer.

2 CHAIRMAN MELIUS: Yes, because I --- 

3 MEMBER ZIEMER: Are you going to present 

4 that criteria at the next Board meeting?

5 CHAIRMAN MELIUS: Yes. I want to get 

6 input that any other Board Members have and sort 

7 of explain our next steps to them. 

8 DR. NETON: I'm hoping that maybe, you 

9 know, the Full Board would see this and agree, and 

10 then we could sort of just finalize this sometime 

11 shortly thereafter.

12 CHAIRMAN MELIUS: Yes. No, that's why 

13 we'll be asking for it, and so forth. And if you 

14 can --- yes, what I would suggest, if you can get 

15 the small revision done and then, Ted, if you can 

16 get it out to the entire Board with sort of a note 

17 to the effect that we're going to, you know 

18 --- we're intending to try to finalize this at the 

19 Board meeting.

20 MR. KATZ: Yes, absolutely. 
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1 MEMBER ZIEMER: So, do we need a formal 

2 recommendation from the Work Group that we are 

3 recommending adoption then?

4 CHAIRMAN MELIUS: Yes, I think that 

5 would be in order, I think. I hadn't thought of it 

6 ahead of time, but I think you're right, that it 

7 would be helpful to have.

8 MR. BARTON: Dr. Melius?

9 CHAIRMAN MELIUS: Yes?

10 MR. BARTON: This is Bob Barton. I 

11 actually just thought of sort of a follow-on 

12 question on this document. It's broad, it's not 

13 necessarily ---

14 CHAIRMAN MELIUS: Okay.

15 MR. BARTON: --- the prescriptive type, 

16 but it's this notion of, you know, you have to take 

17 a look at the actual detection levels of the system 

18 and see if you have missed doses that are just 

19 completely implausible. Jim gave the example of, 

20 you know, perhaps doses based on thorium 
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1 monitoring. And I was just kind of wondering to 

2 myself if we headed down that road in a specific 

3 instance where, you know, all you have is values 

4 that are, you know, less than the detection limit 

5 of the system, and when you apply missed dose 

6 calculations you end up with implausibly high 

7 doses. I guess my question is, where do we head to 

8 from there?

9 DR. NETON: Well, I think, Bob, it's 

10 probably SEC.

11 CHAIRMAN MELIUS: Yes.

12 DR. NETON: Can't do anything with a 

13 coworker model. That's usually the pathway towards 

14 an SEC if you can't reconstruct the unmonitored 

15 workers' doses. That would seem logical to me.

16 MR. BARTON: Okay. I just wanted to 

17 clarify that point because it stuck out a little 

18 bit.

19 CHAIRMAN MELIUS: So, I'm looking for a 

20 motion from the Work Group that we move this forward 
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1 as a recommendation that the Board adopt this.

2 MEMBER BEACH: Jim, this is Josie. I'll 

3 go ahead and make that motion ---

4 CHAIRMAN MELIUS: Okay, thanks.

5 MEMBER BEACH: --- that we adopt the 

6 new---

7 CHAIRMAN MELIUS: And, again, as with 

8 anything, it's a living document. All of our 

9 documents are living documents we've changed as 

10 we've gone along, but I just --- I actually think 

11 it would help as much with sort of the impetus to 

12 make sure that all of our Board reviews this and 

13 thinks about this as we go through it. Obviously, 

14 there'll be other opportunities with specific 

15 examples, but we'd like to get this closed out.

16 MEMBER ZIEMER: I'll second the motion.

17 CHAIRMAN MELIUS: Okay, thanks.

18 MEMBER ROESSLER: Yes. And I think that 

19 by doing that, that puts this in a position for the 

20 Board to know that it's an important document, so 
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1 we could almost call it required reading.

2 CHAIRMAN MELIUS: Yes.

3 MEMBER ROESSLER: If we could that 

4 subtly.

5 CHAIRMAN MELIUS: I can tell former 

6 academics.

7 MEMBER ROESSLER: Can't help it. 

8 CHAIRMAN MELIUS: Should we say there 

9 will be a quiz at the end of it?

10 MEMBER ROESSLER: Yes, right.

11 CHAIRMAN MELIUS: And those are your 

12 first --- day one of our meeting. I'm not sure what 

13 the --- you have to buy us breakfast or something 

14 if you fail, or something like that. I'm assuming 

15 since I've heard from everybody that we all agree, 

16 so the motion passed. So, Ted, could you do that 

17 communication after getting --- 

18 MR. KATZ: Absolutely, yes.

19 CHAIRMAN MELIUS: --- the document from 

20 --- 
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1 MR. KATZ: Yes, and I'll do that 

2 communication. I'll let them know that the Work 

3 Group is recommending this for adoption, and you'll 

4 be putting that to them in the meeting.

5 CHAIRMAN MELIUS: Good. Okay. I think 

6 we're ready to move on to the second part of our 

7 agenda, which is dealing with the --- it's an SC&A 

8 report on the ORAU Report 0055 on, I call it the 

9 exotic nuclides report. It sounds a little sexier 

10 than just comparison of exotic trivalent 

11 radionuclide coworker models. So, we have that 

12 review, and I think we focused --- I believe it was 

13 8-13, if I remember right, in specific parts. And 

14 then yesterday or Friday, I can't remember which, 

15 Jim Neton also sent us out --- it was a draft of 

16 some of the responses to a different special 

17 exposure Evaluation Report that ORAU and Tim 

18 Taulbee put together. And, again, there were 

19 specific parts of their response, numbers 13-16, 

20 19 and 20 that were sort of NIOSH's response to the, 
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1 sort of, the same issue that came up with a 

2 different report. 

3 So, Jim, I don't know how you wanted to 

4 walk through this part.

5 DR. NETON: Yes, I'll try. This has been 

6 going on for a while, so it's a little bit 

7 convoluted, but I'll try my best to explain where 

8 we are, what we want to do here.

9 It turns out that SC&A had reviewed 

10 Addendum 3 to the Savannah River Site Evaluation 

11 Report some time ago, and I had that written down 

12 but I can't find it immediately. Here we go. It was 

13 back in 2012 where SC&A reviewed Addendum 3. 

14 Addendum 3 had a number of issues it 

15 dealt with, including the tritium reconstruction. 

16 It also talks about the trivalent actinide model, 

17 and SC&A in their review came up with a number of 

18 findings on that trivalent actinide model. In the 

19 meantime, NIOSH has issued Report 55, which was the 

20 trivalent actinide model, sort of the 
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1 formalization of what was in the Evaluation Report. 

2 And SC&A reviewed that document, and not 

3 surprisingly they came up with the same findings 

4 in the area of what we want to talk about today, 

5 the six findings.

6 I will say that I looked through all 18 

7 findings, I believe, and it looks to me like 

8 findings 1-7, Report 55, really relate to issues 

9 that are related to the coworker model, and will 

10 in one shape or form be dealt with when we revise 

11 the coworker models in response to what's in the 

12 MDA, so I feel pretty good about that.

13 And then you're left with these 8-13 

14 findings that really --- and I want to talk about 

15 these today because I mentioned sort of our last 

16 area of discussion on the interpretation of 

17 individual measurement values that go into 

18 coworker models. And, specifically, these findings 

19 deal with data below the MDA. All five of these 

20 findings actually --- all six of these findings are 
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1 in that general area. I'd just like to focus on that 

2 a little bit. 

3 In the trivalent actinide set, there 

4 are two kind of sets of data. There are the logbook 

5 entry data, and then there are the OPOS values that 

6 were calculated from the logbook value, so you have 

7 a person that may have been sampled three times in 

8 one year, in some cases she'll have one analysis 

9 that was run 10 separate times to get 10 values, 

10 but that's one urine sample. And then there may be 

11 two more samples that were taken during the year. 

12 Well, then you have actually three values that will 

13 go into the OPOS calculation. The value that was 

14 run 10 times on that one sample is kind of a 

15 different situation, but it's --- SC&A is still 

16 discussing the use of values below the MDA, and I 

17 think it's good to start with the repeat 

18 measurements, the repeat analyses of the 

19 individual samples. To me, it's the simplest case 

20 to start with. 
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1 In those repeat measurements, we have 

2 values that are below the MDA in many cases, and 

3 what we've done, our approach is to use all those 

4 values to calculate the average, and then that 

5 average value would be the OPOS --- one of the 

6 values that goes into the OPOS calculation. 

7 SC&A seems to be arguing that you can't 

8 use those values that are less than the MDA, but 

9 I strongly believe that at least in this case you 

10 should, because if you don't, you end up having a 

11 biased estimate of the mean value. 

12 I put forth on the screen our response 

13 to Finding 13, which is the same as Finding 8 in 

14 the Review of Report 55, the same finding. And I 

15 wanted to highlight our response where there's a 

16 NUREG document, NUREG-1156 that specifically 

17 addresses this issue. And it's pretty clear if you 

18 read the couple of paragraphs that we've excerpted 

19 from that document that you should use values below 

20 the MDA to do mean value calculation. There's 
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1 meaning in those values, so that's our position.

2 Now, that's for the repeat analysis of 

3 the individual --- of a single sample. We 

4 extrapolate that and also use value below the MDA 

5 when you're calculating the OPOS value that uses 

6 multiple samples. And I believe that's also 

7 correct, but I'd like to start with a simple 

8 analysis where you've done say 10 analyses of the 

9 same sample. I appreciate, you know, SC&A's 

10 discussion on why we wouldn't use those 10 values 

11 to calculate the mean value. 

12 Am I on mute?

13 CHAIRMAN MELIUS: No, you're not on 

14 mute.

15 DR. NETON: Maybe I confused everybody.

16 CHAIRMAN MELIUS: No. Anybody from SC&A 

17 want to respond?

18 MR. BARTON: Sure. This is Bob Barton. 

19 Yes, Jim, as you know, I mean, our original concern 

20 going down to the fact that we were even seeing 
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1 negative OPOS values in some situations. Now, 

2 that's largely --- at least the negatives part of 

3 that is addressed in the most recent Report 53, 

4 which basically instructs that if it's negative, 

5 you're going to set it to a censored value of less 

6 than zero. So, that is how the negative values are 

7 being adjusted.

8 We still sort of question --- you know, 

9 like I understand what you're saying about, you 

10 know, these values may have some meaning, but in 

11 the context of an actual dose reconstruction, we 

12 still have concerns about that. And I was kind of 

13 thinking about it, I was trying to put it in sort 

14 of simple terms to, kind of, get out of the weeds 

15 a little bit and see if, you know, it makes a little 

16 bit more sense from a broader view. And this is sort 

17 of how I thought about it.

18 We all agree that the gold standard for 

19 coworker modeling would be if we had the 

20 wherewithal, the time, and the resources, is that 
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1 we'd reconstruct every monitored worker's intake, 

2 and from that we develop a distribution of intakes. 

3 And from that distribution of intakes, we would be 

4 able to assign some level, whether it be 50th or 

5 95th of intake values, but we've agreed that that's 

6 just simply not feasible. So, in actuality what 

7 we're doing is we're taking all these monitored 

8 workers and almost assuming that their OPOS values 

9 belong to that one monitored worker. You see what 

10 I'm saying? You know, it's a surrogate process, 

11 obviously. That's the very nature of coworker 

12 models. 

13 So, when we take these data points, the 

14 raw data and calculate an OPOS value, and then you 

15 have, say a 50th percentile OPOS value for each 

16 period you're looking at, each year, and then we're 

17 going to go ahead and run IMBA to fit that to an 

18 intake for a chosen intake regime, and then we get 

19 whatever the intake is based on those bioassay 

20 samples, which we're assuming belong to the single 
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1 unmonitored claimant.

2 But if you go and look at an actual dose 

3 reconstruction, never are values that are less than 

4 one-half of the MDA, at least that I've seen, used 

5 in an actual dose reconstruction. If you have a 

6 claimant who was monitored for let's say plutonium, 

7 and all of their results are zero and negative and, 

8 you know, less than one-half the MDA, the procedure 

9 is you assume on that last day of sampling a value 

10 of one-half the MDA, and then you calculate a 

11 chronic intake that will result in that bioassay 

12 sample, the last bioassay sample to the claimant. 

13 So, in an actual individual dose reconstruction 

14 we're not taking these values, to my knowledge, and 

15 I might be wrong on this, and using them at face 

16 value. We are, in fact, truncating them at some sort 

17 of potential level, in most cases it's one-half the 

18 MDA. 

19 So, for my money I'm wondering why would 

20 we do it differently in the coworker setting where 
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1 we're really trying to do the same process but using 

2 surrogate data from the entire monitored 

3 population?

4 Now in the case of this SRS exotics 

5 model, when you look at the derived excretion rates 

6 which are based on the OPOS, and they're in TIB-81, 

7 and you look at the 84th percentile for the period 

8 of interest, I believe every single one of those 

9 data points at the 84th percentile excretion rate 

10 is less than the minimum detectable activity. Not 

11 all of them are less than one-half, but a lot of 

12 them are less than one-half. So, what you have is 

13 you have a distribution of OPOS values, the worker 

14 excretion rates for the monitored population that 

15 indicate that the dose was missed. So, I mean, I 

16 guess we're wondering wouldn't it be more

17 appropriate to assess the missed dose based on just 

18 the bioassay samples we have? 

19 You would still be using, potentially 

20 using those negative, not negative but less than 
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1 MDA values in developing the OPOS, but when you go 

2 and start picking off percentiles by year for the 

3 entire monitored population, if those samples are 

4 indicating the monitored population has missed 

5 dose because of whatever reasons, limitations of 

6 a detection system or whatnot, then if the 

7 monitored population will be getting missed dose, 

8 I don't see why the unmonitored population who 

9 should have been monitored wouldn't also be 

10 assigned missed dose.

11 DR. NETON: You raise a good point, Bob. 

12 A couple of things. First, you're right that we have 

13 decided, and we did revise Report 53 to not include 

14 negative values in the OPOS calculation. And that 

15 was done principally because we implemented the, 

16 you know, the backwards time-weighted average 

17 approach, and it didn't make much sense to start 

18 integrating over a short period of time for a 

19 negative intake, so I do agree with that. And I 

20 think we're on board with that concept.
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1 But as far as how we do individual dose 

2 reconstructions and missed dose versus developing 

3 coworker models, I think they're two separate 

4 issues. You're right that we will always use 

5 probably the MDA over 2, I'm guessing, as the best 

6 estimate of the intake, or the best estimate of 

7 excretion in an individual dose reconstruction, 

8 but when you're dealing with a coworker model, 

9 you're talking about potentially hundreds, if not 

10 thousands of data points of OPOS values. And those 

11 tend to even out over time over the large group of 

12 data when you're putting them together, so that you 

13 don't want to start having biased estimates of 

14 intakes in these coworker models.

15 You know, there's --- you know, if you 

16 just --- it's a distribution, just as you said, 

17 it's not an individual dose reconstruction itself. 

18 So, the distributions themselves when you piece 

19 them together will on average come out with a less 

20 biased model. 
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1 Now, as far as affecting people's dose 

2 reconstruction, I would say that you've got to look 

3 at the population we're trying to reconstruct, the 

4 unmonitored workers. Now, there's various reasons 

5 why people were unmonitored. In the extreme case, 

6 though, maybe a worker was --- should have been 

7 monitored and they lost his bioassay records and 

8 he was highly exposed. 

9 I would suggest that truncation --- use 

10 of data below the MDA would not affect the 95th 

11 percentile, very little in those cases, so those 

12 workers would not be disenfranchised by use of this 

13 --- use of the values below the MDA. 

14 The second point I bring up is that the 

15 MDA is really not the value that should be used in 

16 evaluating the data points because that's an a 

17 priori statistic. That value is calculated prior 

18 to having done any measurement. Once you've done 

19 a measurement, you need to implement the a 

20 posteriori statistic which is the decision level. 
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1 It's called other things but let's call it the 

2 critical level, decision level. That's typically 

3 one-half of the MDA, but there is value below even 

4 the decision level. 

5 Again, if I took a blank --- a person 

6 had no plutonium in his urine, he was never exposed, 

7 and I measured it 10 times the subtracted 

8 background, I would expect on average half of the 

9 value to be negative, half of the value to be 

10 positive, and the mean value to be zero. If you do 

11 not do that, if you just only accept the positive 

12 values, then you're going to bias the result high. 

13 Typically, it just makes sense to us that that's 

14 the way we deal with these averages. And we don't 

15 use averages in individual dose reconstruction.

16 I would go back, though, and --- I tried 

17 to focus on these repeat measurements of the same 

18 sample because I think it's most clear in that 

19 situation, though. If I measure the same urine 10 

20 times, I take 10 aliquots of the same urine and 
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1 measure it, and let's say it was a control, the 

2 average value has to be zero if it was not exposed. 

3 And if I don't use the data level below zero, then 

4 I'll get the wrong number. And that's very clear 

5 in this situation where you've got multiple 

6 analyses of the same aliquot, or multiple aliquots 

7 of the same sample. 

8 MR. BARTON: Well, yes, I know you maybe 

9 perhaps didn't want to get into it today, but part 

10 of these findings are a corollary to the findings 

11 about observations we had where samples that were 

12 --- are aliquots of samples that were well above 

13 the MDA still showed a lot of variability which is, 

14 of course, an adequacy concern. But, no, here's I'm 

15 talking about, it's almost a question of parity. 

16 I mean, I under --- it almost seems like we are 

17 biasing towards the monitored worker, which to some 

18 extent makes sense.

19 DR. LIPSZTEIN: Bob?

20 MEMBER BEACH: Yes, I'm sorry, go ahead.
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1 DR. LIPSZTEIN: No, no. I'm 

2 interrupting you, but I just wanted to say that in 

3 all my years of lab work dealing with urine samples 

4 which was the gaze of SRS that we were discussing, 

5 it's very rare that you really have below zero 

6 results. You generally have sort of --- if it's 

7 below the MDA, it's below the detection limit, 

8 you'll have something which is denied of the 

9 instrument. Very rarely you are going to find those 

10 results that are below zero. 

11 And another thing that I was going to 

12 say is that we are working with claimant-favorable 

13 assumptions, so I wanted to read a text from 

14 NCRP-164 which --- because NUREG-1156 is from '87, 

15 and NCRP-164 is a more recent one. And that's what 

16 he --- the document says about bioassay data less 

17 than the limit of detection.

18 It says that bioassay data less than the 

19 limit of detection can lead to biases and they 

20 affect doses in values weight and things in excess. 
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1 This is because that's the main reason we are 

2 looking at the urine samples, because we want to 

3 determine the dose. So, what NCRP-164 says is that 

4 the biases are under --- it's underestimated if 

5 data less than the limit of detection are set to 

6 zero, overestimated if data less than the limit of 

7 detection are set equal to the limit of detection, 

8 overestimated if data less than the limit of 

9 detection are ignored, and only data greater than 

10 the limit of detection are used, and biased by an 

11 uncertain amount if the data less than the limit 

12 of detections are set to an arbitrary fraction of 

13 the limit of detection."

14 So, no place here is talking about the numbers that 

15 are less than zero.

16 You know, the underestimate is already 

17 written here very specifically, "The bioassay data 

18 is underestimated if data less than limit of 

19 detection are set to zero." Imagine below zero.

20 So, what NCRP-164 suggests is that it's 
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1 to do a cause between zero and the LOD, the limit 

2 of detection. And if you want to be, you know, 

3 claimant-favorable, use the LOD because it 

4 overestimates -- or some number in between, but 

5 then there are the uncertainties undetermined, the 

6 bias is very undetermined.

7 DR. NETON: Joyce, I think --- I don't 

8 disagree with what's in NCRP-164. I didn't hear 

9 anything in there that said we couldn't use 

10 values, measured values that were less than the 

11 detection limit.

12 DR. LIPSZTEIN: I'm not saying less than 

13 the detection limit. You are calling negative 

14 values, the ones below zero, but that's an 

15 important distinction.

16 DR. NETON: Well, we're not using 

17 negative values in the OPOS calculation any more. 

18 That --- 

19 DR. LIPSZTEIN: Okay, so that's good. 

20 Okay. That's one point of discussion.
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1 DR. NETON: But we are using ---

2 DR. LIPSZTEIN: We don't want negative 

3 below zero results. So the results will be between 

4 zero and the limit of detection. Right?

5 DR. NETON: We will not use a bioassay 

6 value average that is less than zero in the OPOS 

7 calculation. That is true. But we still believe 

8 in the value in data points that are less than the 

9 detection limit, because when you're doing 

10 averaging one needs to use all the data to calculate 

11 the average value, whether it's an OPOS 

12 time-weighted average, or whether it's the average 

13 of 10 repeat measurements of the same sample. You 

14 still ---

15 DR. LIPSZTEIN: Don't you agree that if 

16 you have values below zero, then suppose there is 

17 something --- in other words, it's below even the 

18 noise of the instrument, it's very rare that you 

19 will have --- we got this at SRS because I don't 

20 believe the data there is good. I think the 
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1 difference ---

2 DR. NETON: I've measured a lot of 

3 samples of environmental samples, plutonium in 

4 human autopsy samples, hundreds, and I can tell you 

5 for certain that many values were below zero 

6 because people didn't have any plutonium in their 

7 system. 

8 So, 50 percent of them --- not 50 

9 percent, but a large number of them -- ended up 

10 being less than zero just by the way, you know, you 

11 subtract background. I mean, if it's background and 

12 you measure it 10 times, background is going to be 

13 below zero half the time, it's going to be above 

14 zero half the time. That's just the way it works.

15 DR. LIPSZTEIN: I'm not --- okay. I 

16 never went into this situation. I always have 

17 numbers that are a little bit more zero. But anyway, 

18 they can't have negative intake, so either zero or 

19 some number above zero. Right? So, when you 

20 translate it into those, it can't have a negative 
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1 value.

2 DR. NETON: That's exactly what we're 

3 doing. 

4 Well, I guess I'd like to look at 164 

5 because I don't have it in front of me, but I didn't 

6 hear anything in there that contradicted what we 

7 would be doing, or what we're doing. 

8 MR. BARTON: Well, I think --- this is 

9 my rudimentary understanding of the passage Joyce 

10 read -- but I think what they're saying is that when 

11 you have samples that are less than the detection 

12 limit that you really want to use more of a 

13 probabilistic curve ---

14 DR. LIPSZTEIN: Exactly.

15 MEMBER BEACH: --- which I believe in 

16 the program is a triangular distribution. If your 

17 minimum is zero, your mode at one-half, which you 

18 mentioned is probably the decision level, and then 

19 the maximum of the triangular is the limit of 

20 detection. So that's sort of, I guess, a sampling 
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1 way to do it, but the easy way to do it just say, 

2 okay, we'll assume it's one-half the MDA, which, 

3 like I said, is done in dose reconstructions. 

4 Which leads me back to something you 

5 said. You said, well, yes, we use that in 

6 individual dose reconstruction, but it doesn't 

7 apply to coworker modeling. You know, we all agree 

8 that the best way to possibly do a coworker model 

9 is to a best estimate dose reconstruction, which 

10 would not use values that are less than one-half 

11 the limit of detection.

12 DR. NETON: I don't know. We disagree 

13 here, I guess. I'm not sure where we --- we're not 

14 going to solve this, I guess, this afternoon on this 

15 call. 

16 DR. TAULBEE: I think this is kind of an 

17 important part for us --- 

18 DR. NETON: Yeah.

19 DR. TAULBEE: --- the coworker models to 

20 move forward on that next step as you're running 
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1 them, Dr. Melius, is taking it -- starting to go 

2 through it. You really need to come to some type 

3 of an agreement with regards to how we calculate 

4 the OPOS.

5 MEMBER BEACH: Well, it may not come to 

6 that, actually. I mean, if you think about it, if 

7 you use the raw data set, taking out the negatives 

8 for the reasons you stated, using pre-weighting, 

9 it just really mucks things up. I mean, you could 

10 still calculate your OPOS value but, I mean, if you 

11 look at it and you see that the OPOS value for a 

12 given year at whatever percentile is less than half 

13 of the limit of detection, I mean, it just seems 

14 logical to me that you would assign a missed dosage. 

15 Because your monitored population is simply 

16 indicating that they would receive missed dose. 

17 And, in fact, that's what they're going to be 

18 getting in their own dose reconstruction. Of 

19 course, we can't do dose reconstruction for all of 

20 them.
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1 DR. NETON: If you have a thousand 

2 measurements, you can actually almost calculate 

3 what people were receiving. I mean, they weren't 

4 receiving missed dose. You have a lot of data. If 

5 you have 84 percent of your values below the MDA, 

6 I can guarantee you statistically it's not possible 

7 all those people receiving the MDA. It's not 

8 possible. They could not all be at the MDA and 

9 --- it's not statistically possible.

10 MR. BARTON: No, I understand that. 

11 You're saying ---

12 DR. LIPSZTEIN: That ---

13 MEMBER BEACH: Go ahead, Joyce.

14 DR. LIPSZTEIN: That's exactly what 

15 NCRP-164 says. If you put everybody equal to the 

16 LOD, you are overestimating the data. But if you 

17 put everybody, you go to zero, you are 

18 underestimating it.

19 DR. NETON: We're not. We're estimating 

20 the best statistic we can, which is an unbiased 
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1 estimate using the data that was measured. Once 

2 you've done a measurement, I have a value. All you 

3 can say, if it's less than the decision level, is 

4 that I'm 95 percent sure that it wasn't a real 

5 sample. That's all you can say. 

6 DR. LIPSZTEIN: I think we’re saying the 

7 same thing. I don't know.

8 DR. NETON: I think we are. I think, if 

9 you look in OPOS, it kind of works out where, you 

10 know, there will be a period of time where the one 

11 OPOS sample will be less than the MDA, but then for 

12 another monitoring period it'll be maybe greater 

13 than the MDA. But you can't compare the OPOS value 

14 to the MDA; you have to compare individual samples 

15 to something. And I think we use the individual 

16 results if we have them, because if you ---

17 DR. LIPSZTEIN: Yeah, you are going to 

18 use the individual results, but you are not going 

19 to use below zero results.

20 DR. NETON: For an individual OPOS 
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1 value, that's correct. Or not an OPOS --- 

2 DR. LIPSZTEIN: Now, to calculate the 

3 OPOS you're not going to use below zero results. 

4 Is that what you are saying?

5 DR. NETON: There will be no results in 

6 the OPOS calculation below zero. That's correct.

7 DR. LIPSZTEIN: Okay. Okay.

8 DR. CHMELYNSKI: This is Harry 

9 Chmelynski. I'd like to make a comment also on this 

10 discussion, which is: the reason why we're seeing 

11 these negative numbers is because we made up the 

12 procedure where we think we know what the 

13 background that we ought to be using for this 

14 measurement is, and we subtract and, hey, we get 

15 a negative, now we're going to pretend that those 

16 negatives mean something.

17 I find it very hard to believe any of 

18 this discussion unless I know how the background 

19 varies from individual to individual, and how well 

20 it was measured, and what did they actually do to 
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1 get those negative numbers.

2 DR. NETON: Well, we have the procedure 

3 at Savannah River. I could show you that. But I 

4 want to go back to the example of the repeat 

5 measurements of the same sample, though.

6 DR. MAKHIJANI: This is Arjun. Could I 

7 say something on the OPOS question, as a general 

8 matter, not Savannah River, before you move on, 

9 please?

10 CHAIRMAN MELIUS: Sure. Go ahead, 

11 Arjun.

12 DR. MAKHIJANI: Jim, I think you and 

13 Joyce were saying different things. Joyce was 

14 asking whether you're going to use negative values 

15 in your calculation of OPOS. And I heard you say 

16 that you would not have negative OPOS results. 

17 Those are two different things.

18 DR. NETON: What I meant to say, Arjun, 

19 was we would not use zero values in calculating an 

20 OPOS result.
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1 DR. MAKHIJANI: So, maybe it was my 

2 understanding, but you're saying that any negative 

3 result would not be used in the OPOS value 

4 calculation.

5 DR. NETON: Correct.

6 DR. MAKHIJANI: Thank you. 

7 DR. NETON: That's already in the 

8 procedure. We revised the procedure a while ago to 

9 state exactly that.

10 DR. MAKHIJANI: Thank you. 

11 DR. CHMELYNSKI: Arjun, I'd like to 

12 point out that I'm not quite sure that everybody 

13 --- I'm sorry, am I on mute now?

14 DR. NETON: No, you're live.

15 MR. KATZ: Harry, we hear you.

16 DR. CHMELYNSKI: Okay. There's two ways 

17 to say that it's not being used in the OPOS 

18 calculation. You can say that it's not being used 

19 in this time-weighted averaging, or you can say 

20 it's not being used at all in the OPOS calculation. 
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1 Well, if you're going to use them when 

2 calculating individual day and time averages then 

3 you're still using them in the OPOS calculation. 

4 DR. NETON: Well, yeah, let's talk about 

5 that. I mean, if we have an individual value, an 

6 excretion value, one measurement, and it's less 

7 than zero, it would not be used in the calculation. 

8 It would be zero. 

9 But I want to get back to the --- this 

10 is sort of unique to this trivalent actinide data 

11 set where you have multiple measurements of the 

12 same sample. That's a very different situation. 

13 You've taken the same sample, I've taken, say, five 

14 aliquots and I chemically processed it five 

15 separate times and measured it five times. It's the 

16 same exact sample. That sample has to follow some 

17 distribution, and if you throw away the negative 

18 values in that analysis, you're throwing away 

19 usable data. 

20 Now, if that average value comes out 
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1 less than zero, we wouldn't use it as less than 

2 zero; it would be zero. But this is only probably 

3 going to occur in this example of the trivalent 

4 actinides. I can't think of any other data set --

5 there probably are -- where we have multiple 

6 measurement of the same sample. 

7 Okay, maybe plutonium at Savannah 

8 River. But, to me, if I measure something ten times 

9 to get the meaningful average, if I did the same 

10 analysis ten times, I have to use all the data. That 

11 just makes common sense to me. How could it not?

12 DR. CHMELYNSKI: If we talk about, then, 

13 the use of OPOS as a time-weighted average, are you 

14 saying that if I use these negatives on a given day 

15 and they do give me a negative OPOS value, what are 

16 we going to do with that value? I think I understand 

17 you to say we're going to set it to zero?

18 DR. NETON: For an individual 

19 measurement that goes into the OPOS calculation it 

20 would be set to zero. That's correct.
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1 DR. CHMELYNSKI: Why not the MDA?

2 DR. NETON: Well, less than zero, but 

3 effectively we would be using zero.

4 DR. CHMELYNSKI: But why not the MDA?

5 DR. NETON: Because we're using the 

6 actual results, the values that are --- we're using 

7 results that are less than the MDA in the 

8 calculation of OPOS. The MDA is an a priori 

9 measurement. It's established before you ever 

10 measure the sample. Once you measure the sample, 

11 it's a different number. It's the decision level, 

12 not the MDA, so ---

13 DR. CHMELYNSKI: So you're saying it's 

14 a number --- it could be any number greater than 

15 zero.

16 DR. NETON: Yes. Yes.

17 DR. MAKHIJANI: This is Arjun. Let's 

18 take another example than radiation. Suppose you 

19 make ten length measurements and then you have two 

20 negative measurements. It's impossible. You have 
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1 to throw out physically impossible results from 

2 your average. You cannot use physically impossible 

3 results in creating an average because it makes the 

4 average meaningless, I think.

5 DR. NETON: No, that's not a good 

6 example, Arjun. You're assuming you have a positive 

7 measurement to start with. If you don't have any 

8 length at all, you measure it, sometimes it's going 

9 to be minus-.1 inch, sometimes it'll be plus one. 

10 It's the net value you're looking at. If I have a 

11 ruler and I measure one inch, and then I measure 

12 it ten times and I'll get 10 measurements, 

13 sometimes it'll be 9.9 inches, sometimes it'll be 

14 0.1 inch. I have to average all those values to get 

15 the true value, which is one inch. If I don't, if 

16 I throw away everything that's less than one inch, 

17 it's going to bias the value. It's a net value that 

18 you're talking about here, net above background. 

19 You measure background.

20 DR. MAKHIJANI: I can't agree. Anyway, 
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1 doesn't --- 

2 DR. NETON: You tell me if I measure one 

3 inch, and I measure it ten times, you're always 

4 going to have values that are larger than one inch.

5 DR. MAKHIJANI: No, no. You should have 

6 always values greater than zero.

7 DR. NETON: No, not if I subtract them. 

8 I want to get the net measurement. I have one inch, 

9 and now I measure something else, and I say is this 

10 bigger than that one inch? And I measure it, and 

11 it's exactly one inch, I'll get a distribution 

12 about the one inch. I subtract the two, and I get 

13 zero with a distribution about it. It's statistics, 

14 that's the way it works.

15 DR. LIPSZTEIN: Yeah, but your --- I 

16 agree with it, but you are going to underestimate 

17 the data when you use --- 

18 DR. NETON: No, you won't underestimate 

19 the data. You'll get closer to the real value.

20 DR. LIPSZTEIN: No, I don't think so. I 
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1 think you are sometimes underestimating the data. 

2 Anyway, let me come back to SRS, the 

3 repeated measurements. Our problem is much bigger 

4 than having negative values. The problem is that 

5 the difference in these results are so big that you 

6 can't really use them. It's not something that is 

7 around the detection level.

8 DR. NETON: No, that's another issue, 

9 Joyce.

10 DR. LIPSZTEIN: Yeah, yeah. Well, 

11 right. And you'll have that on the draft.

12 DR. NETON: That's a different 

13 situation. In fact, I think it's somehow related 

14 to chemical recovery of the measurement technique, 

15 myself, but that's ---

16 MR. STIVER: This is Stiver. I might be 

17 able to jump in a second here. It seems like we're 

18 kind of conflating a couple of different concepts. 

19 I think what Jim is talking about really is the idea 

20 of a null distribution where you're doing multiple 
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1 sampling with your accounting system. And you're 

2 going to get a distribution, if you do a normal 

3 distribution, centered around zero. 

4 And I think Arjun seems to be talking 

5 more about the situation where you actually some 

6 positive analyte that may be --- you know, it's 

7 greater than zero but may be less than the detection 

8 limit. 

9 So I can kind of understand Jim's point 

10 of view when you have multiple aliquots of a single 

11 sample. You're basically --- you know, if there 

12 was nothing in there whatsoever you'd expect it to 

13 be zero. If it was a little bit more than --- you 

14 know, you might have a few negative values, you'd 

15 have quite a few more positives. 

16 You're kind of getting into a situation 

17 where you're moving away from that null 

18 distribution and into a situation where you have 

19 something that's less the MDA, maybe, but still 

20 greater than zero.
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1 DR. NETON: Well, I think we're on 

2 different sides on this. I don't know how to 

3 proceed. Maybe we need to --- 

4 DR. LIPSZTEIN: You said you were going 

5 to take a look at NCRP-164, I think.

6 DR. NETON: Well, I can look at that, but 

7 I just don't see us agreeing on this phone call. 

8 I mean, we both spoke our positions and it's 

9 relatively clear, I think.

10 DR. LIPSZTEIN: Okay.

11 DR. NETON: I'll look at 164. I don't 

12 know that it's going to change our opinion, based 

13 on what I think you read. 

14 DR. LIPSZTEIN: It clearly says you 

15 underestimate the dose if that's less than the 

16 limit of detection --- 

17 DR. NETON: Yes, for a single sample 

18 that may be true, but when you take averages, that's 

19 a different situation. That's what I'm trying to 

20 say. When you're taking average values, you will 
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1 bias your results high if you throw away values that 

2 are below the detection limit. I don't know. It's 

3 what I learned in my first year of graduate school. 

4 I don't think it's wrong, I'm sorry. 

5 DR. MAURO: This is John. I'm conflicted 

6 on SRS, but I want to raise a question which is more 

7 generic, that Arjun asked, and I think I want to 

8 ask it again in a different way. If I have a person 

9 that has quarterly urine samples and one of them 

10 --- and you want to do your OPOS on that, and we 

11 know the procedure. 

12 Now, if one of those urine samples 

13 comes back negative, are you going to assume that 

14 negative number is not negative, but it's zero, 

15 when you do your OPOS calculation?

16 DR. NETON: That's correct.

17 DR. MAURO: You've answered my 

18 question. I, for one, from a purely theoretical 

19 basis and from the same statistic course that 

20 you've taken, I think that's the 
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1 claimant-favorable way to proceed. 

2 If your OPOS ends up, at the end of the 

3 process of doing this, which is less than one-half 

4 the MDL, let's say you end up with that as my OPOS 

5 number for that person for that year, you're going 

6 to use that less than --- it'll be someplace within 

7 zero --- if you do that, that means the number that 

8 you're going to use has the single value that 

9 represents that person for that year, it'll be 

10 someplace between zero and one-half -- well, it 

11 would be someplace between zero and the MDL. 

12 However, it's very possible that it 

13 could come out at some level that's less than 

14 one-half the MDL. Are you going to use that as your 

15 OPOS value? Not zero, but it's not one-half the MDL 

16 either, it's someplace in between there. Is that 

17 what you're going to use?

18 DR. NETON: Yes.

19 DR. MAURO: Yes. Okay, so it's possible 

20 that, in the development of your OPOS value for a 
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1 single person, that the outcome will never be zero. 

2 Well, it'll never be zero, but I guess it'll be some 

3 --- it could theoretically be someplace between 

4 zero and one-half the MDL, and you will use that 

5 value.

6 DR. NETON: That's correct.

7 DR. MAURO: Okay. I just wanted to 

8 understand your position. Now I understand it, and

9 I'll leave it to everyone else to decide whether 

10 they're comfortable with that or not. 

11 MR. BARTON: Well, you know, I certainly

12 --- I think I understand where you're coming from, 

13 Jim, and I think you understand the position here,

14 that in any given year, if the upper percentile of 

15 the average urine concentration for the monitored 

16 workforce in a given year -- for the sake of 

17 argument we'll just say the 50th percentile, that's 

18 most often implemented. 

19 At the 50th percentile of the monitored 

20 worker population, you would have an annual average 
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1 urinalysis result that is less than one-half of the 

2 MDA. I simply feel that the claimant-favorable 

3 thing to do in that situation, even though the 

4 calculations completely reflect the numeric 

5 results of the data we have, I think when you go 

6 to reconstructed dose from that 50th percentile 

7 annual average urine sample, that you should be 

8 applying it at likely the decision level because 

9 that is consistent with individual dose 

10 reconstruction procedures. 

11 I guess that's my piece. I don't want 

12 to harp on it too long.

13 DR. NETON: Remember, Bob, we don’t 

14 assign the 50th percentile, we assign the 50th 

15 percentile with the geometric standard deviation 

16 of the distribution with a minimum of three. 

17 There's where additional --- you know, it’s not 

18 favorableness, but we try to account for the 

19 uncertainty in our value. So, whatever that value 

20 comes out you're going to have a GSD at a minimum 
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1 of three, if not five, or six, or whatever the 

2 distribution ends up being. So, that's what we do.

3 If you all of a sudden say I can't have 

4 anything less than the MDA, and then I start putting 

5 GSDs of five on top of it, you start getting into, 

6 I think, some silly statistics. You're putting 

7 statistics about values that were sort of contrived 

8 to begin with. We're dealing with averages here, 

9 not individual samples. 

10 CHAIRMAN MELIUS: This is Jim Melius. 

11 So, my question is, how much of a practical 

12 difference is this going to make?

13 DR. NETON: That's a good question, Dr. 

14 Melius. I don't know.

15 CHAIRMAN MELIUS: Yeah, and I'm not sure 

16 we can know until --- you know, it depends on what 

17 situations we encounter. 

18 MR. BARTON: Well, it could be 

19 considered comparable to the urinalysis values in 

20 OTIB-81, which were calculated based on just a 
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1 standard OPOS, not the time-weighted. So that's 

2 obviously going to change things. Setting zero OPOS 

3 values, or negative OPOS values to zero, is going 

4 to change those numbers, but based on how they stand 

5 right now, the 50th percentile and the 84th 

6 percentile OPOS urinary excretion rates are well 

7 below the detection limit. And most of them are 

8 actually below one-half the detection limit, or as 

9 Jim referred to it, the decision level. 

10 So, I mean, the way the data stands now, 

11 what we're applying is going to be intakes based 

12 on urine results that are much less than one-half 

13 the detection limit. 

14 Now, that could change based on, you 

15 know, normalizing negative values to zero and such. 

16 We really can't know at this point.

17 DR. NETON: Right.

18 DR. LIPSZTEIN: When we have, for 

19 example, as the case of SRS, when we had the 

20 americium-241 on the disk samples, a result equal 
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1 to the limit of detection, which is .3 dpm, would 

2 result in a dose to the bones of the face, a 20-years 

3 dose to the bones of the face of around 14 rem. So 

4 if you use half of it, it would be 7 rem. So it 

5 may make a difference for nuclides like americium. 

6 DR. NETON: Yeah. You know, you have to 

7 look at these situations where people aren't really 

8 exposed very much. And like Bob has pointed out, 

9 a large percentage of the samples are less than the 

10 MDA, many more than above the 50th percentile. You 

11 still have to extrapolate back somehow to figure 

12 out what your 50th percentile value is, and we've 

13 been doing that by extrapolating backwards, you 

14 know, fitting the distribution to it and saying 

15 here's the 50th percentile. 

16 DR. LIPSZTEIN: I understand. I was just 

17 answering what, in practical, does it mean. I think 

18 we've come, you know, to the way you have pointed 

19 out in the draft, that sometimes the detection 

20 limits go to unrealistic doses, or could not. In 
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1 realistic terms, in terms of dose, for example, it 

2 could make a big difference depending on the 

3 nuclides. And, of course, on the limit of 

4 detection. So, in practical terms, it could be 

5 meaningful.

6 DR. NETON: Yes.

7 MR. BARTON: Could I ask, maybe someone 

8 on the NIOSH side, I don't know this off-hand, but 

9 you kind of mentioned that perhaps the data set that 

10 we're using here, the trivalents, might be a rather 

11 unique situation. 

12 How often do we actually have these raw 

13 data measurements, as opposed to them simply 

14 reported as less than the detection limit? Because 

15 the reason I point this out is we looked at 

16 individual claimant files provided by DOE of 

17 monitored claimants who are in this database, and 

18 those logbook files that contain the raw results 

19 weren't even included. So I would assume it would 

20 be included in the dose reconstruction. 
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1 I'm just curious as to how many 

2 situations do we come across this where we have raw 

3 data points that sort of run the spectrum between 

4 zero and the MDA, in which you would be calculating 

5 OPOS results that would really run the gamut 

6 between zero and the MDA.

7 I don't know if that's information 

8 that's on hand, or if this is a special situation, 

9 or this is something that we might encounter 

10 somewhat frequently?

11 DR. TAULBEE: It really depends upon the 

12 sites. This is Tim Taulbee. It really depends upon 

13 the site where we're working on a coworker model. 

14 And it will completely run the gamut. You are 

15 absolutely correct. In some sites all we have is, 

16 you know, a less than value, and we have no other 

17 information, in which case, from the OPOS 

18 standpoint, we treat it as a censored value.

19 But at some of the sites, thinking of 

20 INL, for example, we have the original worksheets, 
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1 like we do at Savannah River, where you can go 

2 through and you see what the background value is, 

3 and you see what the gross count value is, and 

4 you've got a net value count. 

5 So it really runs the gamut across the 

6 facilities and the sites as to what level of detail 

7 that we have. So I really can't give you a better 

8 feel than that. You know, I can name two big sites 

9 where we do have it, but other sites I'm sure we 

10 don't. Sorry.

11 MR. BARTON: I'm looking at RPRT-0053, 

12 and maybe this statement, either I'm not 

13 understanding it, or it's an error. I can throw it 

14 up on the screen if people want, but it's in 

15 Attachment C on page 43. And Attachment C, it's 

16 Time-Weighted OPOS Method, and it says, "The OPOS 

17 method was designed as the MPM, the Maximum 

18 Possible Mean, of the face values for all censored 

19 and uncensored excretion results for one person in 

20 a year. By face value of a measurement, it is 
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1 understood that measurements reported below the 

2 censoring level are replaced by the value of the 

3 censoring level."

4 And I look at that, and on first read 

5 it sounds like we're talking about exactly that: 

6 you have numerical values, even if they're below 

7 the censoring level, which the censoring level in 

8 this case is 0.3. That's even on the logbook cards, 

9 it's the report value. You have your individual 

10 disk results, you have your normalized disk 

11 results, and then the final column is reported. And 

12 if the average of those normalized disk results was 

13 less than the MDA, it was reported as less than 0.3, 

14 and that's what's contained also in the claimant 

15 records. 

16 So, based on that sentence, it would 

17 sound like the censoring level could be considered 

18 .3, or it could be considered the decision level, 

19 or half that at .15. And that values less than that, 

20 based on the opening sentences of Attachment C, 
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1 seems like the intent was in fact to assume those 

2 values are at some censoring level, which in this 

3 case could either be .3 or .15 depending on whether 

4 you want to use the MDA or decision level.

5 DR. NETON: I'm confused by this. I'll 

6 be honest with you.

7 DR. TAULBEE: Bob, one thing, and I 

8 don't --- I'm trying to figure out if this was 

9 playing a role into why we wrote that in there. 

10 There is MDAs, there’s decision levels and there’s 

11 reporting levels, as well. So, one of the things 

12 I know, at some facilities, we'll have data that 

13 is actually above an MDA but below a reporting 

14 level, where reporting level is considered a 

15 significance level. And so what you'll see in some 

16 of the records will be like a less than what the 

17 reporting level was. So, I'm not sure, I'm thinking 

18 this is --- 

19 DR. NETON: I think what we're seeing 

20 here is the difference between OPOS and 
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1 time-weighted OPOS. If you look at the next 

2 paragraph. Sorry, Tim. It says, "In a time-weighted 

3 OPOS method, the computed statistic is defined as 

4 the maximum possible weighted mean of the face 

5 values for all the censored and uncensored 

6 results."

7 MR. BARTON: So you're saying in the 

8 original OPOS value we would have reset values 

9 below the censoring level, but in the time-weighted 

10 we're using them as-is?

11 DR. NETON: I don't know if that’s true 

12 in the original OPOS. I haven't thought about the 

13 original OPOS in a while. We don't intend to use 

14 it unless there's no other way around it. 

15 Can someone from ORAU speak on this? 

16 Because I don't remember what the approach was. 

17 Nancy or Tom, since this is not an SRS-specific 

18 question.

19 MR. LABONE: Can you summarize the 

20 question? This is Tom LaBone.
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1 DR. NETON: Yeah, Tom. I'm looking at 

2 Attachment C, which was part of the revision to 

3 RPRT-0053, and it says, when it's talking about 

4 calculating an OPOS value, not the time-weighted 

5 OPOS. It says, by face value of a measurement it 

6 is understood the measurement reported below the 

7 censoring value are replaced by the value of the 

8 censoring level. I don't recall. I'm not sure why 

9 that's in there.

10 MR. LABONE: Well, what that means is 

11 that if you have less than 10 that's reported to 

12 us and we go to average it, we basically lose the 

13 less than --- 

14 DR. NETON: Oh, that's right, that's 

15 right. It says, "reported below the censoring 

16 level."

17 MR. LABONE: Well, if you haven't 

18 reported below the censoring level, then you don't 

19 have to worry about the censoring because you have 

20 the actual value.
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1 DR. NETON: That's what I'm saying, 

2 that's not what this sentence appears to say. 

3 That's what's bothering me. 

4 MR. LABONE: I don't have it in front of 

5 me.

6 DR. NETON: Yeah. But at any rate, Bob, 

7 the concept was that if we had the actual value, 

8 we would use the actual value. 

9 DR. TAULBEE: Tom, are you on Live 

10 Meeting?

11 MR. LABONE: No.

12 DR. TAULBEE: Okay, never mind. 

13 DR. NETON: Anyway, the idea, Bob, is 

14 that we would not use the value below the --- if 

15 you have the actual value, it would be used in 

16 either situation. I'm not sure, that sentence does 

17 not seem to say that, but that's not what we're 

18 doing.

19 DR. CHMELYNSKI: It would seem to me 

20 that the term itself, maximum possible mean, says 
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1 that that's exactly what we should be doing, is 

2 replacing anything with a face value below the 

3 limit of detection with the value of the censoring 

4 level.

5 DR. NETON: What we're doing, Harry, is 

6 we're replacing -- anything that's a less than 

7 value is replaced with that value itself. In other 

8 words, if it's less than .3 we would use .3 in the 

9 calculation.

10 DR. CHMELYNSKI: Well, what if it says 

11 on --- to the column to the left of less than .3, 

12 if it says .2, that's the case we're talking about.

13 DR. NETON: Right. We would use .2 

14 because we have the actual measured value.

15 DR. CHMELYNSKI: Well, then why would 

16 you call it the maximum possible mean? I mean, just 

17 because they quote some number less than the limit 

18 of detection, it doesn't mean that number means 

19 anything.

20 DR. NETON: It's the measured value, and 
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1 when you're taking averages of measured values ---

2 DR. CHMELYNSKI: How can you use the

3 measured value below your limit of detection? It's 

4 not a measured value, it's a created construct that 

5 you decided to call a value.

6 DR. NETON: Harry, if you have ten 

7 measurements --- I'll go back to my ten 

8 measurements -- of the same sample ten times, you

9 would not take the mean of all those values to get 

10 my average value. You wouldn't do that. If I had 

11 ten --- 

12 DR. CHMELYNSKI: Well, why are we ---

13 excuse me. Then why are we using the word ‘‘maximum 

14 possible mean’’? 

15 DR. NETON: What?

16 DR. CHMELYNSKI: Why are we using this 

17 catchphrase, ‘‘maximum possible mean,’’ if indeed 

18 it is such a subtle difference?

19 DR. NETON: To replace all of the values 

20 that are less than --- that are listed as less than 
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1 the MDA with the MDA if we don't know what the 

2 measurement was. That's what we're doing.

3 DR. CHMELYNSKI: I understand what 

4 you're saying you're doing, but I would get rid of 

5 this term, ‘‘maximum possible mean,’’ because I 

6 think that measure value has nothing to do with the 

7 value that could have been measured. We could have 

8 had any number between zero, negative, up to the 

9 limit of detection. You just happened to get that 

10 one number.

11 DR. NETON: Harry, if that's the only 

12 objection, I would be happy to do that, and then 

13 we can move forward.

14 DR. CHMELYNSKI: Anyway, yeah, you're 

15 right. We've beaten this to death, so I'm going to 

16 lay off. 

17 CHAIRMAN MELIUS: This is Jim Melius. 

18 Can I suggest a way forward?

19 DR. NETON: Please.

20 CHAIRMAN MELIUS: Okay. And there's 
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1 some, I'll say, urgency here, but we need to be 

2 expeditious about this, I think. In responding to 

3 what Tim was saying before, we do need to be moving 

4 ahead on Savannah River, and that's obviously going 

5 to be affected by the new set of guidance that we 

6 will be talking about at our Board meeting in a 

7 couple of weeks, that we also want to pick an 

8 example to sort of test the guidance, or I guess 

9 pilot test the guidance may be the way of putting 

10 it. And we had talked about possibly that being 

11 Savannah River, but I think Jim and NIOSH staff need 

12 to think about what would be the best one, an 

13 appropriate one to use, and so forth.

14 So, what I would suggest we do is that 

15 we --- well, the other thing that --- this Work 

16 Group will be meeting sometime in the near future. 

17 I'm not sure exactly when, but we also have one 

18 other task we need to deal with based on when an 

19 SC&A report comes out having to do with the Dow 

20 Madison site, so we will be scheduling meetings, 
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1 anyway. 

2 What I was thinking of is when we're in 

3 the State of Washington in a couple of weeks, 

4 everyone will have had time to think about this 

5 issue more, maybe clarify things, and we can either 

6 come back to it in a Work Group, but I think we need 

7 to be able to at least give NIOSH enough --- if the 

8 pilot of the guidance is going to involve looking 

9 at a site where this issue is going to come up, I 

10 think we need to be able to give NIOSH some guidance 

11 on going forward on that. 

12 So, I guess my suggestion is we, when 

13 we're out by Hanford, that we talk and sort of 

14 figure out specific steps at that point in time. 

15 It'll give everyone a chance to think about, one, 

16 NIOSH, what would be the appropriate pilot. And, 

17 secondly, to what extent it's going to involve or 

18 not involve this issue. 

19 And I think we're going to need to 

20 figure out a schedule to expeditiously resolve this 
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1 issue, if we can. But I think we all need to think 

2 about it some more and decide. Does that make sense 

3 to the other Work Group Members?

4 MEMBER ZIEMER: Yeah, that's fine with 

5 me.

6 MEMBER ROESSLER: Fine with me.

7 MEMBER BEACH: Yeah, that works for me, 

8 also.

9 CHAIRMAN MELIUS: I mean, I think the 

10 discussion has been useful. I'm not sure it's 

11 readily resolvable, but we usually manage to 

12 resolve these things.

13 DR. NETON: Dr. Melius, do you suggest 

14 that I bring this up as part of a discussion point 

15 to the full Board, or we just reserve that as a sort 

16 of internal deliberation for the Subcommittee --

17 or the Work Group?

18 CHAIRMAN MELIUS: I would think the 

19 latter, but ---

20 DR. NETON: Yeah, I guess until we have 
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1 a path forward maybe it's not --- 

2 CHAIRMAN MELIUS: Yeah, I mean, I just 

3 think it's hard --- I mean, it's hard to discuss 

4 this even on the phone. I think it's just one of 

5 these issues that --- 

6 DR. NETON: Yeah, I agree.

7 CHAIRMAN MELIUS: --- an in-person 

8 meeting is better, and with some more examples, and 

9 sort of agreement on what documents we're going to 

10 see and so forth. I mean, it's a little hard --- 

11 DR. NETON: And that's why I wanted to 

12 bring this one up specifically today, because in 

13 my mind it's the last hurdle.

14 CHAIRMAN MELIUS: No, I fully agree with 

15 looking at this, but I'm not on the SRS Work Group, 

16 and so, you know, some of this, it's the first time 

17 I'm seeing some of this.

18 DR. NETON: Yes, exactly.

19 CHAIRMAN MELIUS: And I don't know about 

20 all the others involved, between the conflicts and 
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1 everything. So I don't think it needs to be 

2 there, I mean, at the Board meeting presented, but 

3 I do think we sort of need to figure a step forward. 

4 And, John Stiver, if you can talk to 

5 your group and sort of think about this some more 

6 also, it would be helpful. 

7 MEMBER ROESSLER: Jim Melius and Jim 

8 Neton, this is Gen. It would be helpful to me to 

9 see the pertinent pages in NCRP-164 that Joyce was 

10 reading from. And I looked on my shelf, I don't have 

11 it. I think it's one of those that's online only. 

12 So if you find those pertinent pages could you 

13 forward them to the Work Group?

14 DR. NETON: Is that the uncertainty in 

15 bioassay --- I mean, internal dosimetry?

16 DR. LIPSZTEIN: Yes, that's the one.

17 DR. NETON: I've got it here somewhere. 

18 MR. STIVER: I know we have that here at 

19 the office. This is Stiver.

20 DR. NETON: Yeah, I don't have it on my 
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1 shelf right now. We could get that out. Joyce seems 

2 to have it handy. Actually, maybe SC&A could send 

3 out that page?

4 MR. STIVER: Yeah, I think we might even 

5 have the electronic version. I'll have to check.

6 CHAIRMAN MELIUS: Good.

7 DR. TAULBEE: Can you submit that to the 

8 SRDB?

9 DR. NETON: Well, he could submit it to 

10 us, I guess. I don’t know. It’s probably too big 

11 to email. We’ll figure it out.

12 MR. BARTON: I'm having a hard time 

13 hearing you guys. You're kind of breaking up here.

14 DR. NETON: Okay, sorry. Yeah, we'll 

15 take a look at 164. I suspect that it's only a 

16 paragraph or so in there, and my gut feeling is it's 

17 not specific to what we're trying to do here, but 

18 we'll look at it. 

19 DR. LIPSZTEIN: It's Chapter 4. 

20 CHAIRMAN MELIUS: Okay. I think we can 
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1 adjourn. Ted, any further --- 

2 MR. KATZ: No, all good. 

3 CHAIRMAN MELIUS: I wanted to make sure 

4 you're still here, Ted.

5 MR. KATZ: I'm still here. Riveted.

6 CHAIRMAN MELIUS: Riveted, good. We 

7 riveted you to the floor. Right. 

8 Anyway, thanks, everybody. I mean, I 

9 think it has been helpful, even if we didn't reach 

10 full agreement. We did reach agreement on the 

11 other document, so that's good. 

12 DR. NETON: I thought the first part 

13 went swimmingly well.

14 CHAIRMAN MELIUS: Not unexpectedly, the 

15 second part is difficult.

16 MEMBER ZIEMER: Well, we're halfway 

17 there, anyway.

18 CHAIRMAN MELIUS: Yeah. And I do think 

19 it's progress. Anyway, again, thank everybody for 

20 taking the time and joining in, and many of you I 
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1 will see in Hanford in a couple of weeks. And some 

2 of you I think we have a Hanford Work Group going 

3 in a couple of weeks just before the meeting also. 

4 MEMBER ZIEMER: Right. Okay.

5 CHAIRMAN MELIUS: Okay, thanks, again.

6 MR. KATZ: Thanks, everybody.

7 (Whereupon, the above-entitled matter 

8 went off the record at 2:43 p.m.) 

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