dataesr/scientific-paragraphs-categorization huggingface dataset:
Foundation Trust. RE and EB are supported by Cancer Research United Kingdom Grant C5047/ A8385. FCCC: The authors acknowledge support from The University of Kansas Cancer Center (P30 CA168524) and the Kansas Bioscience Authority Eminent Scholar Program. AKG was funded by 5U01CA113916, R01CA140323, and by the Chancellors Distinguished Chair in Biomedical Sciences Professorship. GC-HBOC: The German Consortium of Hereditary Breast and Ovarian Cancer (GC-HBOC) is supported by the German Cancer Aid (grant no 109076, RKS) and by the Center for Molecular Medicine Cologne (CMMC). GEMO: The study was supported by the Ligue National Contre le Cancer; the Association "Le cancer du sein, parlonsen!" Award; and the Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program.
Analyses and patients 5] [26]  The study was done to standards suggested for observational studies, including an independent steering committee (not remunerated for participation), use of an a priori analysis plan, and well-maintained and monitored study databases. 27 hildren's characteristics were cross-referenced between the 2 data sets to avoid duplication. We studied 2 consecutive years of data for each eligible child, aged 5 to 11 years at the time of the index prescription: a baseline year preceding the index prescription date, included for defining potential baseline confounders, followed by an outcome year. We required a recorded asthma diagnosis in the database or evidence of active asthma, defined as 2 or more prescriptions for asthma therapy (controller or reliever) during the baseline year.
In addition, during the outcome year, children had to have received at least 1 asthma prescription in addition to the index prescription. Children were excluded from the study for an ever-diagnosis of any chronic respiratory disease other than asthma or a baseline year prescription for maintenance oral corticosteroid therapy or an ICS/LABA combination inhaler. For analysis 1 comparing small-particle and standard sizeeparticle ICS, we included children prescribed their first ICS (initiation population) or increased ICS dose (step-up population) as beclomethasone *Asthma-related events in the database included all events with a lower respiratory tract code, including all asthma codes and lower respiratory tract infection codes. †Overall control was not an outcome measure in the small-particle ICS step-up vs add-on LABA comparisons (analysis 2).
Study end points Composite measures to evaluate asthma-related outcomes, used in our previous studies,  8, 9, 26  are defined in detail in Table  I. In brief, risk-domain asthma control (asthma control) was defined as no hospital attendance for asthma, acute oral corticosteroid course, or general practice (GP) consultation for lower respiratory tract infection requiring antibiotics; the latter criterion was included because, in †Matching variable (year of index prescription matched AE 4 y; for details, please see this article's Online Repository at www. jaci-inpractice. org). zPossible atopy was defined as any 1 or more of the following: recorded rhinitis diagnosis, rhinitis therapy, eczema diagnosis, eczema therapy. Preschool wheeze was captured through database coding; concomitant rhinitis, eczema, and preschool asthma were captured through database-coded diagnosis or therapy for same.
The NNTs to achieve 1 additional child with asthma control using small-particle ICS were 17 and 5 for the initiation and step-up populations, respectively. In the comparison of step-up strategies in analysis 2, increasing small-particle ICS dose was comparable to adding LABA in a combination ICS/LABA inhaler with regard to both asthma control and exacerbation measures. zPossible atopy was defined as any 1 or more of the following: recorded rhinitis diagnosis, rhinitis therapy, eczema diagnosis, eczema therapy. Preschool wheeze was captured through database coding; concomitant rhinitis, eczema, and preschool asthma were captured through database-coded diagnosis or therapy for same. Preschool was defined as age 1 to 3 y. xThe doses of ICS were standardized to equivalence with small-particle beclomethasone and fluticasone; thus, doses of large-particle beclomethasone and budesonide were halved.
We can speculate, on the basis of the results of the present study, that more children may have demonstrated a best response to ICS step-up if a small-particle ICS had been used as the ICS. Moreover, our longer-term findings provide further information that calls into question the recommendations of UK and international asthma guidelines, which identify add-on LABA as the first-line alternative for stepping up therapy when asthma is not controlled by ICS monotherapy. 37, 39 Properly designed RCTs might be of help to further explore the findings reported here. A limitation of this study is the absence of data on adverse effects of therapy, particularly growth. Information on height is not routinely available from database sources, and height measurements in daily practice are not performed using the rigorous criteria of an RCT.
However, during the study period, similar numbers of children in the data sets were initiated on small-particle beclomethasone (w4%) and fluticasone (w6%). Two other observations argue against large differences between the 2 physician groups in asthma management knowledge: (1) similar (and low) proportions of children had recorded peak expiratory flow readings at baseline, and (2) socioeconomic scores were similar for practices in which small-particle beclomethasone or fluticasone was prescribed. Strengths of the present analyses include the large numbers of children, the capture of clinically relevant outcomes over a full year for each child, and the consistency of findings across main analyses and subanalyses. We matched, in addition to sex and age, on all clinically reasonable indicators of baseline asthma severity available in the database.
METHODS Our hypothesis for analysis 1 was that asthma-related outcomes for children, as for adults, would be similar or better with small-particle ICS, even when prescribed at lower doses, than with standard sizeeparticle ICS. Our hypothesis for analysis 2 was that increasing the dose of small-particle ICS would be an effective alternative for children with persistent asthma who require more treatment than their initial ICS therapy. The study was done to standards suggested for observational studies, including an independent steering committee, use of an a priori analysis plan, and well-maintained and monitored study databases. E1-E3  The members of the steering committee are practicing physicians working in different areas of respiratory medicine who provide varied perspectives and guidance to Research in Real Life (RiRL), functioning in complete intellectual independence.
The group comprises adult practitioners and pediatricians because multiple studies are being conducted in various populations. The steering committee members are not paid for their participation but are reimbursed by RiRL for travel to annual or biannual meetings to discuss multiple ongoing studies that are being conducted by RiRL. The design of the study, analyses and subanalyses, and manuscript represent consensus work of this committee, all of whom are authors on the article together with the research team at RiRL. Although clinical trial registration was not required because this was a noninterventional study, each of the analyses was registered, as a separate study, with registered clinical trials registry numbers NCT01141439 and NCT01697722.
Data sources The data sets we used contain anonymized primary care medical records, data entered by health care providers during patient consultations in the form of Read codes. The General Practice Research Database is a large, well-regarded, and wellvalidated primary care database containing anonymized medical record data from subscribing practices throughout the United Kingdom and used frequently for pharmacoepidemiological research  E4-E7  ; it now forms part of the National Health Service Clinical Practice Research Datalink, the English National Health Service observational data and interventional research service. E8,E9  The Optimum Patient Care Research Database is a newer primary care database, established to rigorous standards, that contains anonymous patient data, including both medical records and patient-completed respiratory questionnaire results, from more than 300 primary care practices subscribing to the Optimum Patient Care respiratory review service.
E10  The value of this database information specifically for studying respiratory diseases has been documented in numerous high-quality publications. E4-E12  The available data for this study spanned 1997 through January 2011 and was approved for use by the General Practice Research Database Independent Scientific Advisory Committee and, for the Optimum Patient Care Research Database, by the Trent Multi Centre Research Ethics Committee. The study protocol was approved by the Anonymised Data Ethics Protocols and Transparency Committee, the independent scientific advisory committee for Optimum Patient Care.
51 (0. 23-1. 10) and the clinical exacerbation rate ratio was 0. 48 (0. 30-0. 77) for 5-to 6-year-olds; the corresponding adjRRs for 7-to 11-year-olds were 0. 76 (0. 45-1. 28) and 0. 87 (0. 60-1. 25), respectively. Baseline asthma severity was greater among 5-to 6-year-olds initiating ICS (data not shown); therefore, we hypothesized that better outcomes with small-particle ICS in this cohort were related to baseline severity rather than age. However, the subanalysis of outcomes split by asthma severity (0 or !1 baseline exacerbations) indicated better results for children with less severe asthma (Table  E3 ). Further exploration of results for risk-domain asthma control by outcome-year ICS dose indicated greater variation in results for 5-to 6-year-olds (compared with 7-11-year-olds) and greater variation in the standard sizeeparticle ICS cohort (Figure  E6 ).
Analysis 2: Unmatched versus matched baselines and results For analysis 2, the unmatched ICS step-up cohort, as compared with both add-on LABA cohorts, received a lower baseline ICS dose and included more children with asthma control and fewer children with severe exacerbations during baseline (data not shown). After matching, the matched add-on LABA cohorts had (similar to the ICS step-up cohort) less severe asthma than did the unmatched cohorts (67%-70% with baseline asthma control vs 58% of the unmatched add-on LABA cohorts). During the outcome year, a significantly greater percentage of unmatched patients in the ICS step-up cohort experienced asthma control and treatment stability as compared with both add-on LABA cohorts (data not shown). Unadjusted exacerbation results were similar for unmatched ICS step-up and ICS/ LABA combination cohorts but better for the unmatched ICS step-up than for the separate ICS þ LABA cohort.
Double matches lost on randomization 1:1: Small-particle beclomethasone n=11 Fluticasone n=335 Patients prescribed ≥50% dose increase of small-particle beclomethasone and/or fluticasone as single script: n=30,555 FIGURE E2. Step-up population: patient selection and matching (analysis 1). Patients in the 2 treatment cohorts were matched on clinically and demographically significant characteristics. †Matching variable (please see above in Online Repository text for details). zPossible atopy was defined as any 1 or more of the following: recorded rhinitis diagnosis, rhinitis therapy, eczema diagnosis, eczema therapy. Preschool wheeze was captured through database coding; concomitant rhinitis, eczema, and preschool asthma were captured through database-coded diagnosis or therapy for same. Preschool was defined as age 1 to 3 y.
Patients in the 2 treatment cohorts were matched on clinically and demographically significant characteristics. BAI, Breathactuated inhaler; COPD, chronic obstructive pulmonary disease; DPI, dry powder inhaler; FDC, fixed-dose combination ICS/LABA; GPRD, General Practice Research Database; IPD, index prescription date; OCS, oral corticosteroid; OPCRD, Optimum Patient Care Research Database; Rx, therapy; Script, prescription; SP-BDP, small-particle beclomethasone dipropionate. Small-particle ICS dose step-up versus add-on LABA to ICS in separate inhalers: patient selection and matching (analysis 2). Patients in the 2 treatment cohorts were matched on clinically and demographically significant characteristics. BAI, Breath-actuated inhaler; COPD, chronic obstructive pulmonary disease; DPI, dry powder inhaler; FDC, fixed-dose combination ICS/LABA; GPRD, General Practice Research Database; IPD, index prescription date; OCS, oral corticosteroid; OPCRD, Optimum Patient Care Research Database; Rx, therapy; Script, prescription; SP-BDP, small-particle beclomethasone dipropionate.
Acknowledgments We thank  Jeremy Brockman  for contributions to the data extraction and analysis and  Francesca Barion, PhD , for assistance with subanalyses (the latter a former employee of Research in Real Life Ltd [RiRL]). We thank  Professor Neil Barnes , MBBS, FRCP, for his contributions to the early discussions when planning the analyses (formerly at  London Chest Hospital, Barts , and  The London National Health Service Trust, London, UK ; no compensation received).
Data acquisition and analyses were funded by  Teva Pharmaceuticals ; access to data from the  Optimum Patient Care  Research Database was cofunded by  Research in Real Life Ltd  (RiRL, design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. The research team at RiRL designed the study, conducted the analyses, and coordinated the writing and revision of the article in collaboration with the study steering committee. Conflicts of interest:  W. M. C. van Aalderen  has received travel reimbursement from Research in Real Life (RiRL); is a member of medical advisory boards of  Mundipharma BV ,  Astra-Zeneca , and  Teva ; and received a traveling grant from  Teva  and a speaker's fee from Forest.
001 2005 (2002-2007) 2004 (2002-2006) <. 001 Years since first asthma code, median (IQR) 3. 4 (1. 6-6. 0) 3. 3 (1. 2-5. 5). 36 3. 1 (1. 3-5. 4) 3. 6 (1. 8-5. 5). 038 Database code for asthma 177 (95. 7) 177 (95. 7) 1. 0 265 (96. 0) 543 (98. 4). 047 !1 prescription in the previous 12 mo Nonsteroidal anti-inflammatory drug 12 (6. 5) 12 (6. 5) 1. 0 26 (9. 4) 29 (5. 3). 019 Acetaminophen 33 (17. 8) 34 (18. 4). 90 56 (20. 3) 106 (19. 2). 71 Recorded %predicted PEF 83 (44. 9) 82 (44. 3) - 111 (40. 2) 249 (45. 1) - %predicted PEF, mean AE SD 106 AE 29 104 AE 31. 25 108 AE 30 110 AE 28. 96 Clinical exacerbations 0 131 (70. 8) 136 (73. 5). 42 188 (68. 1) 376 (68. 1). 26 1 39 (21. 1) 33 (17. 8) 60 (21. 7) 108 (19. 6) 2 9 (4. 9) 12 (6. 5) 20 (7. 2) 45 (8. 2) !3 6 (3. 2) 4 (2. 2) 8 (2. 9) 23 (4. 2) Asthma prescriptions, median (IQR) 4 (3-6) 5 (3-7).
Since the -deep option is known to over-filter sources with SNR greater than 5 and will create negative flux haloes, we took our flux measurements from the maps created using the -faint option. A1885 was recovered with a SNR of ≈4 and hence measuring the flux from the -deep map for this source is not believed to introduce any additional error. The specific filtering mode used for each source is shown in table 2. Atmospheric extinction is automatically corrected for within CRUSH, using an estimate of the most recent tau225 GHz value as measured by the on-site taumeter. However, some of our data (particularly during the Nov. 2012 run) suffered from a faulty tau-meter not inserting the correct measurement into the FITS file. Time-dependent records of the on-site tau values were recorded manually during each run, which were compared to the values recorded automatically in the log files.
We used the AMI-LA to observe seventeen of our sources, with each target visited either two or three times in 2012 (see Table  3 ). Observations consisted of 8 minute integrations sandwiched between 1 minute phase calibrator scans. In the April-June scans sources were themselves used for self-calibration. For the September follow-ups we additionally observed nearby phase calibrator sources. 3C 48 and 3C 286 were observed for use as amplitude calibrators. Data reduction was performed using the local in-house software Reduce. Reduction pipelines were used to apply amplitude and phase calibration, flag for telescope errors and Fourier transform to produce frequency channels. Additional flagging for bad data was carried out interactively through visual inspection of the channel data. Data are written out of Reduce as uv-FITS files which were read into AIPS for deconvolution and cleaning using IMAGR.
Additionally, one of our sources (4C+55. 16) that is included within the OVRO monitoring campaign was also monitored at 15 GHz by the 26m telescope of the University of Michigan Radio Astronomy Observatory (UMRAO;  Aller et al. 1985 Aller et al. , 2014)). The UMRAO monitoring ran between October 1984 and June 2007, hence is not ongoing. However the addition of the UMRAO data to the OVRO and AMI data means that for this source we have well-sampled lightcurves at 15 GHz extending forwards from 1985 to the present.
In Figure  3  we plot the typical percentage deviation from the mean flux for each source, as well as the mean fractional error on the flux (displayed as a percentage). Additional data are provided in Table  1. We see that in all instances of the eleven sources monitored since January   000, 1-29  Figure 3. Dotted line shows the minimum percentage variabilities that would have corresponded to a positive Variability Index (typical deviation from mean flux greater than mean fractional error) for the OVRO sources, sampled at 15 GHz on typically oneweek timescales. The green dashed line shows the actual typical deviation from the mean flux for each source respectively. Note that only A2270 shows significant variation (dashed line above dotted line). Sources are arranged left-to-right by their maximal measured flux at 15 GHz.
The detectable percentage variation is typically much lower for brighter sources, with some deviation from a one-to-one correlation due to uncertainties on flux measurements varying by source. Sources are: A) 4C+55. 16, B) RXJ0439*, C) A2270*, D) A2052, E) RXJ1558*, F) RXJ2341, G) RXJ0132, H) AS780*, I) RXJ1350*, J) A2055, K) A2415, L) Z8276, M) A2627, N) A2390, O) A646 and P) A1348. Note that five of these (indicated with *) were in the initial OVRO monitoring list and hence longer time-baseline data are available, where plotted values are those calculated when the time-frame was matched to the other eleven. See also Table  1. 2013 the typical flux deviation is less than the typical error, hence showing why no real V. I. s were returned. For a non intrinsically-varying population of sources we might expect ≈68% of flux measurements to lie within one standard error of the mean.
Where only a limit on variation is recovered, we normalise by the total monitored period in years (roughly 1 year for the 11 sources added to OVRO list in January 2013). Three sources (4C+55. 16, RXJ0439+05 and Z8193) have historical 15 GHz data preceding OVRO monitoring from either the UMRAO campaign or pointed observations with the Ryle Telescope. We combine these with the OVRO data and find the percentage variations over longer timescales. In Figure  8  we show our calculated absolute percentage variations at 15 GHz as a function of the peak position of the fitted GPS. A mixture of sources increasing and decreasing in brightness is seen. A weak general trend for the highest peaking sources to show most variability is seen. Such a trend is expected, as a higher turnover frequency suggests self-absorption closer to the jet-base and hence emission from smaller scales, which can more easily translate to faster emitted variability.
4 GHz) and C-band (4. 8 GHz) for RXJ1558-14, as well as the more recent U-band (14. 9 GHz) monitoring. Data were compiled from the literature and by downloading FITS images for all available observations in the NRAO-archive and extracting flux measurements using IMFIT. We split the lightcurves into four time-windows, indicated on Figure  10    the 5-to-8 GHz spectrum was inverted 6  , although this is not the case at later times. In the middle panel we plot the SED for RXJ1558-14 with data-points coded to correspond to these four timewindows. In the bottom panel we focus only on the GPS-like part of the spectrum, again coding the data-points although here we additionally show illustrative GPS-models to the data in each of the time windows. As we move through time windows A to D we see the GPS-peak appear to move both downwards in flux and also in turnover frequency.
2012). The highest frequency data in window D (and a lesser degree, window C) appear discrepantly high and may be indicative of a new period of activity that will become apparent at lower frequencies only as the emitting knots move down-jet and the self-absorption turnover peaks to lower frequency. VLBI monitoring of the source may therefore detect new knots forming and propagating outwards in the coming decade. In further analogy to NGC1275 we note that RXJ1558-14 has a steep spectrum power-law tail to low frequency. In NGC1275 this tail is associated with a 300kpc radio minihalo. A similar structure may be present around RXJ1558-14. However, we note that in the TIFR GMRT Sky Survey (TGSS) imaging of this source there appears to be reasonably symmetric structure extending almost 200 kpc from the core (PA ≈ 91.
8) and hence the steep spectrum emission may be indicative of large scale lobes from a pre-vious large outburst. Interestingly, X-ray cavities are detected by Chandra in this source  (Hlavacek-Larrondo et al. 2013; Russell et al. 2013 ) with total physical extent of 17. 1±2. 3kpc. Due to its relatively good long-term SED monitoring we consider RXJ1558-14 as a case study in Figure  8. We calculate the GPS-peak frequency and measured variability in RXJ1558-14 for each of the four epochs highlighted in Figure  10. We can directly measure the 15 GHz percentage variability in epoch D only, during which OVRO monitoring data are available. For each of the other three epochs, we measure the C band (4. 86 GHz) and X band (8. 44 GHz) values at the epoch boundaries, using these to to extrapolate an estimate of the 15 GHz flux.
ACKNOWLEDGMENTS We thank the anonymous referee for useful comments and suggestions that have greatly improved this work. MTH acknowledges the support of the  Science and Technologies Funding Council (STFC)  through studentship number  ST/I505656/1. ACE acknowledges support from  STFC  grant  ST/I001573/1. CR acknowledges the support of  STFC. ACF and HRR acknowledge support from  ERC Advanced Grant Feedback. We wish to thank the staff of the  OVRO  for allowing us access to data from their monitoring campaign and additional data for the sources they kindly added to the observing schedule. We would also like to thank the observatory staff at the  MRAO (AMI) , IRAM-30m (GISMO), CARMA, the  JCMT  ( SCUBA-2 ) and the GISMO team for their help in preparing and obtaining data. The OVRO 40-m monitoring program is supported in part by  NASA  grants  NNX08AW31G  and  NNX11A043G , and  NSF  grants  AST-0808050  and  AST-1109911.
and Eileen L. Norris Foundation , the  University of Chicago , the  Associates of the California Institute of Technology , and the  National Science Foundation. Ongoing CARMA development and operations are supported by the  National Science Foundation  under a cooperative agreement, and by the CARMA partner universities. The research leading to the IRAM-30m results has received funding from the  European Commission Seventh Framework Programme  ( FP/2007(FP/ -2013 ) ) under grant agreement No  283393  ( RadioNet3 ). This research work has used the  TIFR GMRT Sky Survey  (http://tgss. ncra. tifr. res. in)data products.