dataesr/scientific-paragraphs-categorization huggingface dataset:
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Medical personnel represent the largest group of workers occupationally exposed to ionizing radiation. Although the health risks associated with occupational exposure to low doses of ionizing radiation in the medical field have been investigated in several national cohorts, no study has been conducted in France to date. The ORICAMs (Occupational Radiation Induced Cancer in Medical staff) cohort is a nationwide French longitudinal cohort of medical workers exposed to ionizing radiation aiming to investigate the risk of radiation-associated cancer and non-cancer mortality. The ORICAMs cohort was set up in 2011 and includes all medical personnel monitored for ionizing radiation exposure with at least one dosimetric record in the SISERI database (the national registry for monitoring ionizing radiation exposure in workers) over the period 2002-2012.
Description of the cohort The ORICAMs study is a retrospective cohort including medical workers exposed to ionizing radiation in the course of their work in France. Occupational exposure to ionizing radiation of workers in France is registered in SISERI, the national system for occupational dosimetry registration maintained by the IRSN, over their entire career. To be included in ORICAMs, workers had to have at least one dose recorded in SISERI (also including zero doses) between January 2002 and December 2012. The temporal criterion enables to overcome the limited data present in French registers before 2002. It also allows to collect data reflecting contemporary working exposures and conditions. Furthermore, workers were included if vital status and cause of death, if any, were recorded in national registers.
Vital status and outcome determination The ORICAMs cohort is composed of 164,015 workers for whom the vital status is known. Workers were included if they were between 18 and 70 years of age at the start of the followup. For each worker, the follow-up started at the date of the first dosimetric record in the SISERI database or on Jan 1, 2002, whichever was later, and ended on Dec 31, 2013 or at the date of death if it occurred earlier. Vital statuses and medical causes of death were respectively obtained from the French National Register of Identification of Physical Persons (RNIPP, which is managed by the National Institute of Statistics and Economic Studies (ISEE) and compiles vital status of people born in France) and from the French National Institute of Health and Medical Research (CepiDC, which aims to collect every medical cause of death in France since 1968), respectively.
Radiation exposure assessment Medical workers are monthly or quarterly (according to their level of exposure) monitored by external passive dosimetry using Thermo Luminescence Dosimeters (TLD), Radio Photo Luminescence (RPL), or Optically Stimulated Luminescence (OSL) dosimeters worn at chest level under the lead apron, and considered as the whole body exposure. In addition, if there is a risk of exposure at the extremities (e. g. , hand, wrist), extremity dosimetry is added to this passive whole-body dosimetry using TLD dosimeters. Dosimetric information or exposure measurements are regularly sent by the various data suppliers to the SISERI system via a secured internet access, using a strictly defined protocol and transmission formats. Individual dose equivalent Hp  (10)  is estimated from badges with a recording threshold of 0.
Statistical analysis We conducted external comparisons using standardized mortality ratios (SMRs) and 95% confidence intervals (CI) to compare the mortality in the cohort (observed number of deaths) with that in the French general population (expected number)  [15]  for all-causes and specific causes of death. The number of expected cases were calculated based on the data provided by the CepiDc. SMRs were standardized by age (1-24, 25-34, 35-44, 45-54, 55-64, 65-74 and 75-84), calendar year  (2002-2006, 2006-2010 and 2010-2013) , and gender (male and female). The expected number of deaths were obtained by multiplying the number of person-years in each gender-, age-, and calendar year-specific stratum using French mortality rates from 2002 to 2013. SMR analyses were performed in R (R Foundation for Statistical Computing, Vienna, Austria) using the package popEpi.
In the future when individual dose assessment will be completed, dose-response analysis will be performed. We plan to use a nested case-control study design to be able to collect additional information on lifestyle factors in occupational medical records for cases and controls. For this purpose, all professionals who died of a disease of interest in the cohort will be included as cases in the case-control study and will be randomly matched with 5 controls on sex, year of birth, and date of cohort entry. Occupational exposure to ionizing radiation will be provided by SISERI, and additional information on potential risk factors will be provided by worker's occupational medical records (e. g. , weight, type of work, smoking status, alcohol intake, predisposition, etc). The strengths of this study include the large sample size of the cohort involving medical workers exposed to ionizing radiation between 2002 and 2012 in France, a design conducted at the national level, and the use of national registries that ensure accurate and unbiased data collection.
Conclusion This study is the first analysis of the ORICAMs cohort of medical radiation workers and showed a significantly lower mortality compared to the general population, which may reflect the HWE. The individual ionizing radiation dose available for each worker were not used in this study. Since it is not feasible to use dosimetric data directly from the databases as it is necessary to take into account the detection threshold, the periodicity, and whether the exposure has changed over the years, the analysis of the dose for each individual is currently underway. Nevertheless, in future analyses of the cohort, the dose-response analysis will be assessed, as in the international project.
Data collection Semi-structured interviews were conducted in French by one or both interviewers who were a pediatric resident (DR) and an obstetrician-gynecologist resident (EF). Data collection spanned from June 2020 to March 2021. Interviews were semi-structured, with a prede ned list of open-ended questions focusing rst on the information received concerning the hospitalization, treatments, and prenatal counseling, and then on desired improvements, and open suggestions. The interview guide was developed by authors (DR, EF, NM, IE and KL) after a review of the literature before starting the study. Interviews were initially conducted in person at a private o ce space located in the NICU, but then because of restrictions due to the Sars-CoV2 pandemic, we conducted interviews over the phone.
To ensure reliability, we used the same interview guide in every interview. Sessions were recorded with the consent of each participant and then transcribed verbatim and de-identi ed. The aims and rational for the research were disclosed to the participants in the newsletter. We con rmed patient's understanding during the interview. Throughout the session, the moderator summarized and reformulated the results and presented them back to the participants to ensure information was accurate and that their comments had been correctly understood. At the end of the session, participants completed a short quantitative questionnaire to obtain their socio-demographic characteristics. We obtained remaining socio-demographic data from the patient electronic medical record.
Data analysis The analysis procedure was conducted by three researchers (EF, DR, NM and IE) using an inductive approach to identify themes that emerged from the data. Each transcript was independently read several times to facilitate immersion in the data. Following this, the researchers used open coding process to summarize participants' views by assigning words to quotes or paragraphs. The coding of the two researchers were then compared and in the event of any discrepancies or a disagreement, other physicians (MY, KL, LL and PP) adjudicated. This method enhances the validity of the assigned themes. We kept including participants in the study until saturation was achieved (ie no new themes or ideas were generated by subsequent interviews). A list of themes and sub-themes was then generated and extracted in tabular form.
Participants We conducted a total of 15 interviews, which took place between June 2020 and March 2021. We obtained data saturation after 12 interviews. Initially we let participants choose between a face-to-face interview or over the phone according to their convenience. Two of them decided on a face-to-face interview, and the last one over the phone. For all remaining participants, we only conducted interviews over the phone due to Sars-Cov2 sanitary restrictions. The duration of the interviews is on average 44 minutes ± 11 minutes (minimum 25 minutes, maximum 66 minutes). Face to face interviews lasted 32 and 42 minutes each. Participant's characteristics are presented in Table  1. On average, participating mothers were 31. 4 years old (± 4. 9 years). Newborns were on average 30. 2 ± 2. 5 weeks of gestation at birth.
Thematic analysis Seven themes were extracted from our data analysis (presented in Table  3 ). We subdivided each theme into sub-themes and illustrated some of them with participants' quotes from the interview. Participants recalled being told about neonatal complications. They talked about respiratory outcomes rst, short and long terms. Then neurological complications were evoked including speci c follow-up and neurosensorial risks. Mothers also reported receiving information concerning the NICU: the rooms, the equipment, the incubator. They remembered being told about the usual medical course and the steps during hospitalization.
Methods We used data from a nationally representative longitudinal survey conducted in Kenya as part of the Performance Monitoring for Action Project  [9]. A multistage stratified cluster design was used to draw a probability sample of households and females of childbearing age  (15-49 years). Women who met this eligibility and who were usual members of the household or who slept in the household the night before were approached for interview. After providing informed consent, 9478 completed a baseline questionnaire in phase 1 (P1) from November 2019 to February 2020. Of these women, 8729 women aged 15 to 48 years (e. g. , who did not age out) consented to participate in future surveys. Of those, 6935 women were relocated 1 year later in phase 2 (P2), reflecting a retention rate of 79. 4%.
Discussion Our study used longitudinal survey and retrospective calendar data to quantify differential contraceptive use patterns between covert and overt users in need of contraception. While we had hypothesized that covert users would be at an increased risk of discontinuation compared to overt users, our results suggest a more nuanced and dynamic story. Somewhat surprisingly, we found that over approximately the first 2 years of use, there were no differences in time to discontinuation between covert and overt users. However, after the first 2 years of use, covert users had an increased hazard of discontinuation compared to overt users, a pattern that persisted until the end of the 5-year observation period. Of note, while most of the overt users remained overt users between P1 and P2, 50% of P1 covert users had transitioned to overt use by P2.
This finding reiterates the importance programs and environments that facilitate and encourage overt use of contraception where feasible and safe. Our study has several limitations. First, the use of the direct question to measure covert use likely underestimates the true prevalence  [31] ; given the sensitive nature of the behavior, women who use contraception covertly may similarly be reluctant to report use in a survey. Second, we collected data on covert use at the time of the P1 and P2 surveys; however, the measure of time to discontinuation included use time prior to the P1 survey, on which we do not have information on covert status. Third, our analytic sample of women is skewed to long-term users due to the inclusion criteria of being in need at P1 and P2, and therefore, we are missing both covert and overt users who adopted and discontinued quickly (e.
Furthermore, a core data set (e. g. , similar to the ESA bed rest core data) to assess musculoskeletal adaptations during space flight could be an efficient tool to streamline research in the last decade of the ISS. In this context, the standardized collection of medical data would be of great benefit to the space life science community while saving resources. Another factor to consider is to increase efforts to facilitate multidisciplinary discussion among astronauts, physicians, crew surgeons, and technical personnel (increment science coordinators, payload specialists) along with space agencies and musculoskeletal researchers. Finally, space sciences need to open up to better statistics  78  , mathematical models, and digital twins to enable model-based prediction of musculoskeletal changes at the individual level.
unclassified, O. Bacteroidales since evidence for a causal effect was weaker in the main MR analyses). Genomewide data for microbial traits were only available from the Flemish Gut Flora Project (FGFP) GWAS limiting analyses to 2223 participants. Additionally, unlike in the mGWAS meta-analysis, neither lead SNP (rs4988235 and rs116135844) reached genome-wide significance in the FGFP dataset. Therefore, focusing on the difference between the tested hypotheses (see Methods), colocalisation results provided little evidence that G. unclassified, O. Bacteroidales was likely to share a causal variant with overall or site-specific CRC (Table  2 ). Regional association plots confirmed findings from genetic colocalisation for G. unclassified, O. Bacteroidales (Fig. 3 ; Supplementary Fig. 1 ). Whilst there was evidence for genetic colocalisation for G.
Bacteroidales microbial traits. All of the 57 associated with overall CRC risk and all but one SNP associated with all site-specific CRC risk were available in the FGFP GWAS summary-level data for these two microbial traits (i. e. , leaving 30, 16, 39 and 30 independent SNPs associated with distal colon, proximal colon, colon and rectal CRC sites for reverse MR analyses). Results gave little evidence that overall CRC or CRC sites had a causal effect on either microbial trait, with CIs spanning the null for all IVW-derived estimates (Supplementary Table  16 ).
Materials and methods Study design. Two-sample MR was used to examine the causal relationship between features of the human gut microbiome and both overall and site-specific CRC risk. In brief, SNP-exposure and SNP-outcome associations were obtained from independent non-overlapping GWASs to generate causal effect estimates of 14 microbial traits on overall and site-specific CRC risk. This study and all methods have been conducted in line with the Strengthening the Reporting of Observational Studies in Epidemiology MR (STROBE-MR) reporting guidelines for MR studies  31. All methods have been carried out in accordance with relevant guidelines and regulations. Gut microbiome GWAS data and instrument selection. Genetic variants associated with microbial traits were obtained from one of the largest microbiome GWAS (mGWAS) meta-analyses of bacterial abundance, presence (versus absence), alpha-and beta-diversity and enterotype conducted within the FGFP (n = 2,223) and two independent validation cohorts (the Food-Chain Plus study [FoCus, n = 950] and the PopGen study [n = 717])  21.
Full details of the data sampling, preparation and analyses have been described previously  21, 32, 33. Briefly, DNA was extracted from frozen fecal samples provided by participants and hypervariable regions of the 16S rRNA gene were amplified and sequenced (the V4 hypervariable region for FGFP and the V1-V2 hypervariable regions for both FoCus and PopGen). Resulting sequences were analysed per sample using the DADA2 pipeline to provide taxonomical classifications of all bacteria in each sample  34. Bacterial classifications that were not confidently assigned at the genus level were organised into arbitrary unclassified groups within higher classification levels. Informed consent and appropriate ethical approval were obtained for each study. In the FGFP cohort, which was used the discovery cohort in the GWAS analysis, the DADA2 pipeline yielded 499 taxonomical units across five levels of microbiota phylogeny from phylum to genus.
Four metrics on alphadiversity (within-individual diversity) and beta-diversity (inter-individual differences) were estimated using data for all 288 genus-level taxa. An enterotype phenotype, describing the different phylum-level community compositions, was also estimated. After performing taxa-level quality control (QC), 92 taxa were available for the mGWAS (details previously described  21  ). Given the ecological count nature of the 16S rRNA data, many microbial traits had zero-inflated distributions, which is problematic for linear modelling. Of the 92 taxa, 62 contained substantial zero-inflation; therefore, these taxa were modelled using a two-step hurdle binary analysis, which includes a binary presence (versus absence) analysis (denoted throughout as "P/A") and a zero-truncated rank normal transformed abundance analysis (denoted throughout as "AB").
All other taxa were relatively normally distributed; therefore, were treated as AB phenotypes and rank normal transformed accordingly. Thus, a total of 159 microbial traits (i. e. , 62 P/A, 92 AB, 3 alpha-diversity, 1 beta-diversity and 1 enterotype phenotypes) were analysed in the mGWAS conducted in the FGFP cohort. Individuals from the FGFP cohort were genotyped on the Human Core Exome v1. 0 and the Human Core Exome v1. 1. Imputation was conducted using the 1000 Genomes data (phase 3) as the reference panel. After variant-level and individual-level QC followed by imputation, 7,711,310 variants and 2259 individuals remained, 2223 of whom also had data on the gut microbiome and all covariates used in the mGWAS analysis. All individuals were of European ancestry. All mGWAS analyses were adjusted for extraction type and year, aliquot year, person performing the aliquot, library preparation plate, the first 10 genetic principal components, sex and age.
1 ) and full information on genotyping, imputation and QC have been described previously  35, 36. Data for CRC sites were available across colon cancer (N = 32,002), which is a sum of distal CRC, proximal CRC and cancer cases with unspecified sites, distal CRC (N = 14,376), proximal CRC (N = 15,706) and rectal cancer (N = 16,212). CRC case status was diagnosed by a physician and approximately 92% of participants were white and of European ancestry. Data access was granted and the study was approved by the GECCO Coordinating Centre. Statistical analyses. Two-sample Mendelian randomization. In our primary analyses, we performed twosample MR using the TwoSampleMR 37 package (version 0. 5. 6) in R to examine the causal relationship between 14 microbial traits and both overall and site-specific CRC risk.
Summary-level data (i. e. , the SNP name [rsid], effect allele, other allele, effect allele frequency, beta coefficient, standard error, p-value and sample size) for each of the 14 microbial trait-associated SNPs associated were extracted from both the mGWAS and CRC GWAS meta-analysis. The proportion of variance explained (R 2 ) in each microbial trait by each SNP and the strength of the instrument (assessed through the F-statistic) were calculated. For each binary microbial trait, the proportion of variance explained of the liability to presence (versus absence) was obtained using the "get_r_from_lor" function of the TwoSampleMR package  37  (the estimate of which was then squared to get the R 2 ). The required parameter for this function describing prevalence of each microbial trait was obtained from FGFP alone (as published by Hughes et al.
21 ). For continuous microbial traits (i. e. , abundances), the R 2 was calculated using the following formula  38 :  where β is the effect size (beta-coefficient), MAF is the minor allele frequency and SE(β) is the standard error of the effect size for a given SNP, and N is the sample size of the mGWAS. The F-statistic was calculated as follows: where R 2 is the proportion of variance explained in the microbial trait by the SNP, N is the sample size of the GWAS, and k is the number of SNPs included in the instrument (i. e. , for our main analysis, k = 1 for each microbial trait)  39. The exposure and outcome datasets were harmonized such that the effect of each SNP on the exposure and outcome was relative to the same effect allele. For ambiguously coded SNPs (i. e. , "palindromic" SNPs where the effect/other allele were either an A/T or G/C combination), we used the effect allele frequency to resolve strand ambiguity, where possible.
We manually searched SNPs in two databases (PhenoScanner and the IEU OpenGWAS database) to identify traits they have previously been associated with. Thirdly, to formally test potential pleiotropy, we selected genetic variants to proxy microbial traits at a more lenient p-value threshold of 1 × 10 -05 and repeated MR analyses with several complementary, pleiotropy-robust methods. Lastly, to assess whether any causal effect observed between microbial traits and CRC in our main analyses was indicative of reverse causation, we additionally performed two-sample MR in the reverse direction (i. e. , with overall and site-specific CRC as the exposure and microbial traits as the outcome). Each of these sensitivity analyses are described in detail below. Colocalisation. Colocalisation analyses were conducted using the 'coloc' R package with default parameters (i.
We used a posterior probability threshold ≥ 0. 80 to indicate evidence of a shared common causal variant between each microbial trait and CRC. Full summary statistics were only available for the FGFP cohort for gut microbiome variation; therefore, genetic variants ± 1 Mb of the lead SNP associated with any microbial trait for which there was evidence for a causal impact on CRC in our main analyses were extracted from FGFP and GECCO genome-wide datasets. Regional association plots were generated to visualise genetic colocalisation using the LocusCompareR package  41. Manual exploration of pleiotropy. Where there was evidence for a causal effect of any microbial trait on CRC risk, summary-level GWAS data collated by PhenoScanner and the IEU OpenGWAS were searched to identify whether any SNP used as an instrument for those microbial traits had been reported to be associated with CRC or any other trait that could be an independent cause of CRC.
A lenient p-value threshold of 1 × 10 -04 was set as the multiple testing threshold at which we defined evidence for an association between the SNP and any disease or trait in PhenoScanner and the IEU OpenGWAS  22, 42. This multiple testing correction was based on the number of results returned by the online PhenoScanner interface (i. e. , 1000, as of May 2022) and for consistency in the inclusive p-value threshold selected across the two platforms, where a 10% Bonferroni correction was applied due to the likelihood that traits in those databases were correlated. Two-sample MR using a lenient p-value threshold to select genetic instruments. To allow the use of more formal pleiotropy-robust methods that require multiple genetic instruments, a more lenient p-value threshold of 1 × 10 -05 was used to expand the number of SNPs associated with each of the microbial traits in the main analyses, reflecting the p-value threshold commonly used in the literature  24, 25.