dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to  Dr. Brigitte Rakotosolofo Bakololao  ( Direction de l'Assistance sociale et de la santepublique ) for providing access to death registers. Pierre Cantrelle  initiated the systematic analysis of data from the BMH. The digitization of the registers was funded by  INED  ( Institut National d'Etudes Demographiques)  and the present analysis was carried out thanks to a fellowship from the  Belgian National Fund for Scientific Research (FNRS). We also thank the reviewers for their suggestions and comments.
This original map was created using ArcGIS 10 software. mountainous part of the catchment (Fig. 3a ). In November 2013, the supply of freshly eroded and contaminated material was clearly observed in upper parts of the same catchment (Fig. 3b ). In upper parts of the Ota River catchment, source of contamination material seems to have dried up (Fig. 3a-b ). It might have accumulated behind Yokokawa dam, where the presence of a thick sequence of strongly contaminated material was shown to have deposited  6. However, the occurrence of the Yokokawa dam release in autumn 2013 led to the clear dispersion of part of this strongly contaminated material to the coastal plains (Fig. 3b ). Finally, in the Ukedo River catchment that was initially fully located within the restricted area and where we only had access -to the outlet only -from May 2013 onwards, deposition of strongly contaminated material was documented in November 2013, which shows that this river draining the most contaminated part of the radioactive plume also provides a continuous supply of radioactive sediment to the Ocean (Fig.
Acknowledgments This work has been supported by  CNRS  in the framework of the  NEEDS-Environnement HAMUSUTA  project, and by the  Institute of Environmental Radioactivity in Fukushima University. This work has also been funded by  ANR  in the framework of the  AMORAD  project ( ANR-11-RSNR-0002 ). Fieldwork assistance by  N. Loffredo  and  Y. Wakiyama  was greatly appreciated.
Study design This was an international, multicenter, open-label, phase 3b study (trial registration: NCT00775437) conducted at 14 sites in the US and the EU. A central institutional review board or independent ethics committee approved the study at each site; the study was conducted in accordance with the International Conference on Harmonization good clinical practices and the Declaration of Helsinki. Prior to any study-related procedures, the informed consent statement was reviewed, signed, and dated by the patient's parent. The size of the study was determined based upon a commitment to the US Food and Drug Administration (FDA) to provide safety and efficacy data for adalimumab when used in 2 to <4 year old patients with JIA, and, in accordance with the FDA, the proposed sample size for this study was approximately 30 patients.
Qualified patients received adalimumab subcutaneously (24 mg/m 2 , maximum 20 mg/dose) eow for a minimum of 24 weeks in a clinical setting. The continuation of prior JIA treatments, such as MTX, stable doses of nonsteroidal antiinflammatory drugs (NSAIDs) and/or low-dose corticosteroids (equal to prednisone ≤0. 2 mg/kg/day) was permitted. In the US, patients could continue in the study until reaching age 4 and ≥15 kg; in EU, patients could continue in the study for up to 1 additional year after reaching age 4 and ≥15 kg to allow time to transition to an appropriate treatment. Upon completion of the study, patients who transitioned to commercially available adalimumab were offered the chance to participate in a long-term JIA registry (STRIVE, NCT00783510). Patients had a screening visit, baseline visit, and visits at weeks 2, 4, 8, 12, 16, 20, and 24.
Statistical analyses The intent-to-treat (ITT)/safety population included all patients who had received at least one dose of adalimumab. Safety data are summarized up to 120 weeks of treatment: AEs are reported as number and percentage of patients affected and as events per 100 patient-years (E/100 PYs). Efficacy data are presented based on observed values (weeks 12, 24, 60, and 96) and nonresponder imputation (NRI) (weeks 12 and 24). Growth data was analyzed as observed.
Acknowledgement The authors would like to acknowledge  Neelufar Mozaffarian, MD, PhD , formerly of  AbbVie , for her significant contributions to the study, interpretation of data, and revisions of the manuscript. Jessica L. Suboticki, PhD , of  AbbVie ,  Kathleen V. Kastenholz ,  PharmD, MS,  of  AbbVie , and  Theresa J. Peterson, PhD , formerly of  Abbott Laboratories , assisted with drafting and revising the manuscript with direction from the authors. Grant support AbbVie funded the study. Disclosures DJK has received clinical trial support from  AbbVie. BB-M has received research grants and consulting fees from  AbbVie. GP, VA, JK, and HK are full-time employees of  AbbVie  and may hold AbbVie stock and/or stock options. AbbVie funded the study ( NCT00775437 ), contributed to its design, and was involved in the collection, analyses, and interpretation of the data and in the writing, review, and approval of the manuscript.
SSHs have been used to evaluate the mortality impact of large global health initiatives such as the United States President's Emergency Plan for AIDS Relief  [6]  or to estimate the number of deaths due to conflict or genocide  [7] [8] [9] [10]. SSHs are also increasingly used to estimate pregnancy-related mortality  [11] [12] [13]  and monitor progress towards Millennium Development Goal 5  [14]. The use of SSHs to estimate adult mortality has long been debated among demographers. There are two main concerns: SSHs may suffer from sample selection biases  [15] , and they may be affected by reporting errors. Sample selection biases emerge when individuals at risk of experiencing an event (e. g. , death) do not all have the same probability of being included in a dataset. This occurs in SSHs because  (1)  individuals who died during the risk interval are necessarily excluded and (2) several siblings can potentially report on the same family.
If adult mortality is associated with family size, then estimates of adult mortality rates will be biased. Analytical corrections for sample selection biases have been proposed  [16, 17]  and evaluated  [18]. Reporting errors refer to situations where the SSH reported by a survey respondent differs from the true survival experience of his/her sibling(s). Such errors have a significant impact on estimates of adult mortality rates  [19]. Demographers have primarily sought to diagnose reporting errors in SSH datasets  [20]. Only recently have they tried to correct such errors through analytical procedures. In particular, researchers working on the Global Burden of Disease 2010 study proposed an innovative adjustment method, the ''corrected sibling survival'' (CSS) method, based on the comparison of SSH data reported in consecutive cross-sectional surveys  [17].
To do so, we modified the standard SSH questionnaire used during DHS by  (1)  including supplementary interviewing techniques (e. g. , recall cues) designed to prevent omissions of siblings in SSHs and (2) adopting an event history calendar format that has been shown to improve reports of ages and dates in demographic surveys. We then conducted a validation study of this new SSH questionnaire, which we call the siblings' survival calendar (SSC), in Niakhar, Senegal. Niakhar has been the site of a Health and Demographic Surveillance System (HDSS) since 1962, which has collected prospective data on adult mortality since its launch. It thus provides a high-quality reference dataset to measure the accuracy of the SSH data. We hypothesized that the SSC would improve the quality of adult mortality data, relative to the DHS questionnaire.
Overview of Study Design This is a retrospective validation study of SSH data collected during surveys in the lower-middle-income country of Senegal. It estimates the accuracy of two survey instruments (the SSC and the standard DHS questionnaire, both described in detail below) by comparing the SSH data they yield to a prospective dataset on adult mortality, the Niakhar HDSS (reference dataset). We first selected and enrolled a stratified sample of individuals who had ever been registered by the Niakhar HDSS. In doing so, we oversampled individuals who had experienced at least one adult death among their maternal siblings, according to the HDSS. We then randomly allocated study participants to either an interview with the DHS questionnaire (control group) or to an interview with the SSC (experimental group).
Measurement Tools for Adult Mortality Reference dataset. Activities of the Niakhar HDSS started in 1962 in eight villages of the Niakhar area and were later expanded to 30 villages in 1983. An initial baseline census was carried out in 1962, followed by another census in 1983, when the study area was expanded. Since these censuses, data on demographic events have been collected from household informants during household visits. Study interviewers use a printed roster of household residents and inquire about the vital status of each household member, as well as possible changes in marital status and births since the previous household visit. New household members (including in-migrants) are added to the roster. Each individual who ever resided in the study area since the start of the HDSS has been assigned a unique ID number, under which HDSS data are stored.
From 1962 to 1987, household visits were conducted yearly. From 1987 to 1997, household visits were conducted weekly because of requirements of vaccine trials conducted in the Niakhar area  [22] [23] [24]. Between 1997 and 2007, household visits were conducted every 3 mo, and between 2008 and 2013, every 4 mo. In the Niakhar HDSS dataset, the date of birth of a population member is ascertained in one of two ways. It is recorded prospectively if s/he is born in the HDSS population between The date of other vital events (e. g. , deaths and migrations) is ascertained through household visits, when changes in household membership are recorded. The household informant is asked to report the day and month when a death/migration occurred. The dates of vital events are thus known with varying degrees of precision, depending on the frequency of household visits.
For each event, the age at the time of the event is calculated as date of event minus date of birth of the individual. Migrants who move to another household of the HDSS study population are assigned a new residence and continue being part of the longitudinal followup under their same ID number. On the other hand, migrants who move outside of the HDSS area (e. g. , Dakar) are lost to follow-up: they are not tracked, and their relatives are not asked to report their vital status. As a result, it is not known whether they are still alive at any time after their migration. If a migrant returns to the HDSS area after some time outside, s/he is reassigned his/ her previous ID number so as to avoid duplication of individuals in the HDSS dataset. The Niakhar HDSS allows researchers to identify sibships (i.
e. , brothers and sisters having the same biological mother) among individuals who have ever resided in the HDSS population. The identification of sibships is possible because every population member is potentially linked to his/her biological mother through a mother ID number. The mother ID number is attributed either at the time of birth (if the mother gave birth in the HDSS area) or the first time an individual enters the HDSS population (i. e. , initial census or after in-migration). We used these data to identify the sibships of potential respondents and measure the rate of omissions of siblings' deaths in each study group (see below). For some members of the HDSS population, the mother ID number may be missing because their biological mother may never have been a member of the HDSS population or because the information reported during household visits was not sufficient to establish a link between mother and child.
Recall cues were designed using findings from a validation study of SSH data conducted in Bandafassi, a HDSS site in southeastern Senegal  [32]. During this study, we found that maternal siblings were more likely to be omitted during SSH interviews if they were deceased, had migrated away from the community of origin, had a different biological father than the respondent, or had not coresided with the respondent  [19]. We thus developed a set of recall cues that addressed these factors. The interviewer introduced cues to the respondent one at a time, asking him/her whether s/he may have omitted one or more maternal siblings who correspond to a cue. Finally, we adopted an event history calendar approach to collecting data on ages at, and dates of, vital events that affected the siblings of a respondent.
Event history calendars are designed to help respondents accurately report the timing of past events  [33] [34] [35] [36]. Such calendars reduce recall error and significantly increase data reliability  [36] [37] [38] [39] [40] [41] , but they have not been used in the collection of SSHs. The event history calendar we designed is a large grid (A3 paper) bracketed by years from 1953 to 2013. It is composed of three sections: a landmarks section, a respondent section, and a siblings section. Landmarks are events that are likely to be remembered by most respondents. These include, for example, major political events (e. g. , independence of Senegal), natural disasters (e. g. , droughts), and sporting events (e. g. , Senegal reaching the quarterfinals of the 2002 World Cup). Because SSH data are most frequently collected during national surveys, we included only national events as landmarks, rather than also including more local events.
Study Participants Study participants were selected among individuals who had ever been registered by the Niakhar HDSS. Individuals were eligible if they were aged 15 to 59 y on 1 January 2013 according to the HDSS dataset and they had at least one known sibling in the HDSS dataset. We excluded members of the HDSS population who had no known siblings or who had a missing/invalid/ inconsistent mother ID number in the HDSS dataset. The SSH data these individuals may report during a retrospective SSH survey cannot be validated against the HDSS data. Study participants included individuals who resided in the Niakhar HDSS area at the time of the survey, individuals who were temporarily absent from their residence in the HDSS area, and individuals who had permanently migrated outside of the HDSS area prior to the survey.
Participant Sampling Primary sample. The study enrollment process is summarized in Figure  1. First, we identified all individuals who met eligibility criteria among individuals ever registered by the Niakhar HDSS. The selectivity resulting from age-and sibship-related criteria is described in Table  S1. Then, we identified sibships in which at least one adult death had occurred over the course of the HDSS and sibships in which there was no known adult death among known siblings. We selected all sibships with adult death(s) for inclusion (n = 592), then we selected an additional 500 sibships at random among sibships in which all siblings registered by the HDSS were still alive. Since adult mortality is a relatively rare event, this Concordant SSH data are those in which a respondent reported a sibship composition that matches the sibship composition in the HDSS dataset (e.
Analysis The analyses we report here are exploratory, previously unplanned analyses of the validation study data. They focus on (1) metrics that are commonly used by demographers in evaluating the quality of SSH data and (  2 ) simple sibship-level comparisons between the reference HDSS dataset and the SSH data collected using the SSC versus DHS questionnaires. We chose to conduct such analyses because they permit a direct comparison between our validation study results and data quality assessments conducted with data from national DHS  [20] , hence allowing an assessment of the external validity of study results. In contrast, the planned analyses of the validation study data focus on outcomes measured at the sibling level by record linkage, i. e. , matching the report of a particular sibling's survival obtained through SSH to the record of that same sibling's survival in the HDSS dataset.
We measured accuracy by the concordance-at the sibship level-between SSH data reported in each study group and the Niakhar HDSS dataset. For example, let us consider a respondent who has one adult sister who died in the last 15 y according to the HDSS. The SSH reported by this respondent is said to be ''concordant'' if the respondent also reported an adult death of one of his sisters during that time frame. The SSH is ''discordant'' if the respondent did not report any adult death or reported only adult deaths among his/her brothers. On the other hand, among respondents whose known adult sisters were all still alive according to the HDSS, an SSH was deemed ''concordant'' if the respondent did not report any adult death among his/her sisters. We thus defined the sensitivity of SSH data as the proportion of concordant SSHs among respondents with at least one adult death among their sisters/brothers according to the HDSS dataset.
Sensitivity was measured separately by gender of the deceased. We measured the sensitivity of each questionnaire in capturing adult deaths that occurred in the 15 y prior to the survey. We selected this reference period (15 y) because this corresponds to the reference period used in analyses of the global burden of disease relying on SSH data  [3]. The specificity of SSH data was defined as the proportion of concordant SSHs among respondents with no adult death among their sisters/brothers in the past 15 y according to the reference HDSS dataset. We measured the specificity of SSH data because we were concerned that the SSC may lead to false reports of deaths among siblings during SSHs. Such false reports may happen because of repeated probing and prompting (e. g. , additions of cousins or other relatives of the respondents).
Participants The study was conducted between 15 January 2013 and 27 March 2013. In total, 609 participants were assigned to complete a DHS questionnaire, and 580 a SSC questionnaire (Figure  1 ). On average, participants had approximately six known siblings according to the HDSS dataset (Table  1 ). The most recent adult death recorded by the HDSS among the siblings of a respondent occurred on average 10. 0-10. 7 y ago for the sisters of respondents and 11. 7-11. 9 y ago for their brothers. Among siblings who had died within 15 y of the surveys, the corresponding figures were 6. 5-6. 9 y for deceased sisters and 7. 2-7. 3 y for deceased brothers. The average age of participants was close to 34 y old. More than a third of the participants were interviewed outside of the HDSS area. The geographical distribution of study participants is shown in Figure  2.
Finally, Figure  3C  shows heaping in reports of date of death. In the DHS group, there was significant heaping 10, 20, and 25 y before the survey (again similar to what was observed in the 2010/2011 DHS). Such heaping was not present in the SSC group. Effects of the SSC on data accuracy. The SSC had significantly higher sensitivity in recording adult female deaths than the DHS questionnaire (Table  2 ). Among respondents in sibships with at least one adult female death in the past 15 y according to the HDSS dataset, 75. 6% reported a concordant SSH in the DHS group versus 89. 6% in the SSC group (p = 0. 027). We did not detect a significant difference in specificity between the DHS and SSC questionnaires in recording adult female deaths, but equivalence tests suggested that this difference was less than 5.
For example, SSC interviews with migrants were conducted last, after close to 2 mo of fieldwork. Interviewers may have experienced significant fatigue at that time, and may thus have been less likely to follow SSC instructions (e. g. , sensitize respondents, use recall cues). Similarly, we collected the information necessary for migrant follow-up during our first passage in each study village (i. e. , at the time of the DHS interviews). As a result, migrant follow-up was conducted in the DHS group within 3 wk of the collection of migration data versus. 6 wk in the SSC group. This gap between collection of migration data and migrant follow-up may have led to errors in recruitment. This may have affected the composition of the pool of migrants we were able to trace for SSC interviews. For example, the proportion of migrants interviewed in Dakar was similar for both male and female respondents in the DHS group (62.
Study Limitations Our work has several important limitations. First, the reference dataset (i. e. , the Niakhar HDSS) we used to evaluate the accuracy of SSH data is not a gold standard. Data on the composition of sibships may be inaccurate, incomplete, or missing when sibship members were born outside of the HDSS area. Similarly, deaths occurring to sibship members who have migrated outside of the HDSS area (or who have never resided in the area) are not recorded by the HDSS. These limitations of the reference dataset imply that our estimates of the specificity and sensitivity of SSH data in recording adult deaths may sometimes be biased. For example, in sibships where the HDSS did not record any adult death, some adult deaths reported by respondents may have occurred outside of the HDSS area or may have been missed during the course of HDSS follow-up.
The limitations of the reference dataset, however, affect both groups of the validation study in a similar manner and should not affect our assessment of the effectiveness of the SSC. Second, the analyses we report here are unplanned exploratory analyses of the validation study data. As a result, they are more likely to yield false positive results (e. g. , detecting a difference in sensitivity between SSC and DHS questionnaires when, in fact, such a difference does not exist). The likelihood of false positive results was also increased by the fact that we tested multiple hypotheses, e. g. , we tested the effects of the SSC separately by gender of the reported sibling. Third, the statistical power of some of the tests we conducted was limited. For example, we could not investigate heaping of ages of death above 40 y because of limited sample size (Figure  3 ).
However, training effects cannot be ruled out entirely: interviewers may have taken some time reacquiring SSH interviewing skills during the first few days of fieldwork in the control group. Future validation studies should explore using study designs in which interviewers are randomly allocated to study groups (i. e. , SSC versus DHS). Finally, we could not investigate whether improvements in data quality/accuracy translate into more accurate estimates of adult mortality indicators such as 45 q 15 , i. e. , the probability of dying between the ages of 15 and 59 y old. Indeed, it is not possible to directly compare the adult mortality rates obtained from different SSH questionnaires to the HDSS data because of the censoring of HDSS data for migrants (see above). In that context, comparing SSH and HDSS data requires identifying exactly which sibling reported during a SSH was lost to follow-up in the HDSS because of migration.
Such record linkages  [19]  are necessary to make the HDSS and SSH datasets comparable by either excluding siblings lost to HDSS follow-up or imputing a censoring date in the SSH datasets. Instead of this direct comparison of adult mortality indicators between SSH and HDSS data, we explored the potential impact of the SSC on estimates of the proportion of respondents with at least one adult death in their sibship. This parameter is an important component of adult mortality indicators. Indeed, the total number of deaths at ages 15-59 y old reported during a survey (denoted 45 D 15 , i. e. , the numerator of adult mortality rates) is the product of (1) the proportion of respondents who report at least one adult death in their sibship and (2) the average number of adult deaths reported per sibship.