dataesr/scientific-paragraphs-categorization huggingface dataset:
Phenotyping. In total, results are reported for 79 quantitative traits and 21 binary traits. These traits were chosen to represent different domains of health and reproductive success, with consideration given to presumed data availability. Many of these traits have been the subject of existing genome-wide association metaanalyses (GWAMA), and phenotype modelling, such as inclusion of relevant covariates, was copied from the relevant consortia (GIANT for anthropometry, EGG for birth weight, ICBP for blood pressures, MAGIC for glycaemic traits, CHARGE-Cognitive, -Inflammation & -Haemostasis working groups for cognitive function, CRP, fibrinogen, CHARGE-CKDgen for eGFR, CHARGE-ReproGen for ages at menarche and menopause, Blood Cell & HaemGen for haematology, GUGC for urate, RRgen, PRIMA, QRS & QT-IGC for electrocardiography, GLGC for classical lipids, CREAM for spherical equivalent refraction, Spirometa for lung function traits, and SSGAC for educational attainment and number of children ever born).
The m fixed covariates included in each model were chosen with reference to the leading GWAMA consortium for that trait and are detailed in Supplementary Data 8. For all traits, these covariates included: age (and/or year of birth), sex, and at least the first 10 principal components of the genomic relatedness matrix (GRM). Where necessary, additional adjustments were made for study site, medications, and other relevant covariates (Supplementary Data 3). For reasons of computational efficiency, it was decided to solve Eq. (  10 ) in two steps. In the first step, the trait (y) was regressed on all fixed covariates to obtain the maximum likelihood solution of the model: y ¼ Xb þ ε′ : ð11Þ All subsequent analyses were performed using the vector of trait residuals ε′, which may be considered as the trait values corrected for all known covariates.
(  13 ) applied to the relevant sex. For all traits, a corresponding estimates of β F SNP and β F GRM were obtained from the models ε′ ¼ μ þ β F SNP Ã F SNP þ ε; ð13bÞ ε′ ¼ μ þ β F GRM Ã F GRM þ ε ð14Þ and the effects of different ROH lengths and of SNP homozygosity (Fig. 4b ) were obtained from the model ε′ ¼ μ þ β 1 Ã F SNP outsideROH þ β 2 Ã F ROH<5Mb À Á þ β 3 Ã F ROH>5Mb À Á þ ε : ð15Þ Effect size estimates for binary traits. Binary traits were analysed by two methods. The primary estimates of β F ROH (Fig. 3b  and Supplementary Data 10) were obtained from full logistic models: g E y ½ ð Þ¼Xb ; ð16Þ where g() is the link function (logit), and where F ROH and all applicable covariates (Supplementary Datas 3, 8) were fitted simultaneously. Mixed modelling for family structure was not attempted in the logistic models since an accepted method was not apparent.
Religiosity and educational attainment as additional covariates. To assess the importance of potential social confounders, proxy measures of socio-economic status and religiosity were separately included in Eq. (  13 ) as additional covariates. The modified effect estimates ( b β′ F ROH ) were tested for significance (Supplementary Data 20) and compared to the uncorrected estimates (β F ROH ) (Supplementary Fig. 10a, b ). Since Educational Attainment (EA) was measured in many cohorts, this was chosen as the most suitable proxy for socio-economic status. However, since F ROH is known to affect EA directly  6  any change in β F ROH when conditioning on EA cannot be assumed to be entirely due to environmental confounding. The analysis of religiosity was only carried out in UKB, where a rough proxy was available.
Although no direct questions about religious beliefs were included, participants were asked about their leisure activities. In response to the question Which of the following do you attend once a week or more often? (You can select more than one), 15. 6% of UKB participants selected Religious Group from one of the seven options offered. In the models described, religiosity was coded as 1 for those who selected Religious Group and 0 for those who did not. Although this is likely to be an imperfect measure of actual religious belief it is currently the best available in a large dataset. Assortative mating. Humans are known to mate assortatively for a number of traits including height and cognition  37  , and so we sought to investigate if this could influence our results, for example, by the trait extremes being more genetically similar and thus the offspring more homozygous.
Acknowledgements This paper is the work of the  ROHgen consortium. We thank the  Sigma   T2D  Consortium, whose members are detailed in Supplementary Note 3. We thank the  UK Biobank Resource , approved under application 19655; we acknowledge funding from the  UK Medical Research Council Human Genetics Unit  and  MRC   Doctoral Training Programme in Precision Medicine. We also thank  Neil Robertson ,  Wellcome Trust Centre for Human Genetics, Oxford , for use of his author details management software, Authorial. Finally, we thank all the participants, researchers and funders of ROHgen cohorts. Cohort-specific acknowledgements are in Supplementary Data 2; personal acknowledgements and disclosures are in Supplementary Note 2. We thank  Rachel Edwards  for administrative assistance. Directed consortium: T.
Data availability The meta-analysed data which support these findings are available as Supplementary Data files. Cohort-level summary statistics underlying all figures and tables are available in a publicly accessible dataset (https://doi. org/10. 6084/m9. figshare. 9731087). In the majority of cases we do not have consent to share individual-level data, although for UK Biobank this is available on request from https://www. ukbiobank. ac. uk/.
Source of epidemiological data All patients with CRC who underwent KRAS mutation status determination between 2008 and 2014 at the Regional Molecular Biology Platform at Lille University Hospital were included in our cohort. The retrospective data collected were demographic data: sex, age, canton of residence at the time of primary diagnosis; the KRAS mutational status: absence/presence of mutation and type. The data were deidentified. This study was approved by the CCTIRS (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé, approval n°1 4. 728) and is in full accordance with French laws.
Ecological regression In order to analyze the association between geographical variations of KRAS mutation proportion and the ecological variables, we used an extension of the previous hierarchical Bayesian model that includes the two ecological variables (French EDI and distance to the CWIP) as fixed effects in the model with a non-informative prior as suggested by Lawson. 24 First quartile of each ecological variable was considered as the reference. Each quartile was assigned an RR and its BCI at the 95% level. Smoothing of KRAS mutation proportions and ecological regression analyses were carried out using the R software v. 3. 4. 3 (package: R-INLA). 25 The cluster detection was performed using the SaTScan software v. 9. 6. 26 Maps were performed using QGIS software v. 2. 18. 27 All statistical analyses were considered significant at 0.
36 Over a 6-year period, our study may have only included less than one-third of the total CRC patients with KRAS mutations. The recruitment of KRAS status via a reference center for a test routinely prescribed by the other centers suggests the representativeness of the study population compared to the overall regional population. Nevertheless, if in some centers KRAS mutation determination was more frequently requested in non-metastatic patients, the prevalence could have been increased superficially resulting in a greater number of patients to be included in our study. Another bias could be relocation if a significant number of patients migrated from another region since, at the time of diagnosis, we recorded the place of residence and not the geographical history of patients. In the ecological study, no significant association was highlighted between KRAS mutations and the distance to the CWIP and between KRAS mutations and deprivation index but few socio-economic and environmental data were available.
In 2015, the International Federation of Gynecology and Obstetrics established the prevention of exposures to environmental reprotoxic substances as a priority for health professionals. However, available information about reproductive hazards is voluminous, dispersed, and complex, and this is a severe limitation for physicians to incorporate the prevention of environmental exposure into standard preventive care. One difficulty frequently cited by physicians is the lack of evidence-based information. The objective of our study was to identify a list of environmental chemical hazards to reproduction. We used lists present in relevant regulations or included in scientific reports or databases to identify reproductive hazards. The reproductive hazards were prioritized according to the strength of evidence concerning their impact on fertility or development of the offspring.
Identification of Chemical Reproductive Hazards Environmental reproductive hazards include biological hazards, toxic chemicals, physical agents, and biomechanical or organizational demands that could harm sexual function, fertility, or in utero or postnatal development of the offspring. Here, we focused only on chemicals. Indeed, clinicians are generally familiar with the biological and physical risks. We also chose to exclude medical treatments that cannot be considered to be environmental exposures. Therefore, we included drugs when they could lead to occupational exposures in medical or research environments. We identified environmental reproductive hazards using lists present in relevant regulations, scientific reports, or databases coming from recognized health agencies. All such classifications are based only on the intrinsic hazard of the substances and do not integrate the likelihood of exposure or the idea of risk.
The DEMETER Database The DEMETER (Documents pour l'Évaluation Médicale des produits Toxiques vis-à-vis de la Reproduction) database was developed by the French National Research and Safety Institute for the Prevention of Occupational Accidents and Diseases (INRS) to summarize information about reproductive hazards. The database incorporates information sheets written by toxicology experts based on bibliographic studies. Human and animal data are differentiated. The weight of evidence concerning adverse outcomes on reproductive health is specified for each chemical  [13]. We only considered substances with limited or sufficient evidence from studies in humans and/or experimental animals.
• Category 3 contained low-priority chemical risk factors for which adverse reproductive outcomes have been observed in human or animal studies or in vitro experiments but appeared in only one classification. As mentioned previously, some selected classifications already included a prioritization of the substances' intrinsic toxicities (lists from the CLP regulation, the NTP-OHAT monographs, DEMETER data sheets, and the NIOSH report). Some other classifications did not do so by default (lists from Proposition 65 and EDs). We considered the weight of evidence for this last category to be low.
Category 1 included the following: Substances or mixtures classified by the CLP Regulation as reproductive toxicants in categories R1A and R1B. Compounds identified in the DEMETER database for which there was sufficient or limited evidence of adverse health effects in epidemiological studies or sufficient evidence documented in robust animal studies. Toxicants studied by the NTP-OHAT with clear evidence of adverse developmental or reproductive effects in humans or laboratory animals. Substances listed in the report produced by the NIOSH. Categories 2 and 3 included the following: Substances or mixtures classified by the CLP Regulation as reproductive toxicants in category R2 or as a mutagen in categories 1A or 1B. Toxicants registered on the OEHHA list. Compounds identified in the DEMETER sheets for which there is sufficient or limited evidence of adverse health effects in animal studies.
From the list established by the NIOSH, we obtained 216 drugs for which studies in animals or humans have demonstrated an adverse effect on the fetus. From the data sheets of the DEMETER database, we obtained 38 substances with limited or sufficient evidence from studies in humans and/or with sufficient evidence from studies in experimental animals and 44 substances with limited evidence from studies in experimental animals. From the priority list of chemicals developed within the EU-Strategy EDs, we obtained 194 substances.
Thus, in Category 1, all the 98 substances extracted from Proposition 65 and the 15 chemicals from the EU list of EDs were recorded in other classifications as well. Almost all the substances studied by the NTP-OHAT were also identified as harmful by the CLP regulation or the DEMETER Database. Table  2  shows all the chemicals identified as Category 1 "high-priority chemical risk factors". We divided the compounds by eight classes to facilitate the analysis of the results: drugs, metals and metalloids, pesticides, organic solvents, synthesizing and/or processing agents in industrial processes, phthalates, perfluorinated compounds, and other compounds. Although only chemicals from Category 1 have been presented in Table  2 , supplemental Table S1 including the full list of hazards from all the categories with their CAS numbers is available on request.
Main Findings Our method allowed us to identify 1251 environmental reproductive hazards from two regulatory lists and four health agency scientific reports or databases. Our prioritization approach resulted in a high-priority classification for 462 risk factors. These substances belong to the following eight classes of chemicals: drugs (n = 206), metals and metalloids (n = 116), pesticides (n = 38), organic solvents (n = 27), synthesizing and/or processing agents in industrial processes (n = 23), phthalates (n = 13), perfluorinated compounds (n = 13), and other compounds (n = 26).
Indeed, we based our analysis on validated and harmonized data to ensure adequate risk management. However, we could have included some of these chemicals, hence assigning them a low level of evidence. However, according to the Classification and Labelling Inventory database available on the ECHA website, a total of 4699 substances are classified as reprotoxic (R1A, R1B, or R2). There are also 924 substances classified as a mutagen (M1A, M1B, or M2). This information can be taken into account on a case-by-case basis in clinical practice by consulting the ECHA website [20].
To the Editor, In their recent work, Martin et al. 1  proposed an open source online tool to help update systematic reviews. The authors use a combination of machine learning and crowd-sourcing approaches to propose and assess trials that might need to be included in the update. Bibliographic databases and the ClinicalTrials. gov registry are searched for new trials to complement the updates proposed by registered users. We believe that this work provides a very useful tool for facilitating and automating some parts of the systematic review process, which are usually time-consuming. The proposed interface is clear, user-friendly, and easy to navigate; the authors make their data freely available for registered users, so that it can be re-used in future research. We believe that this work is of high importance, and we would like to ask for some clarifications and to provide some suggestions on how to improve the system.
CROWD-SOURCING Crowd-sourcing can be an efficient way of collecting human annotators' input for a particular task, but it has its drawbacks. First of all, we would like to clarify how the quality of the contributions is controlled. The system allows only registered users to contribute, to avoid "noise" and random votes. To register, a user needs to answer some questions. However, the details on these questions and their impact on the quality control are not revealed, neither do we know whether any further requirements to qualify as a user exist. We suggest making the registration process more transparent. One common way of crowd-sourcing quality control is to inject some gold standard data points at random intervals in the dataset and to check that they have been properly processed by the users (see eg, https://www.
ucomp. eu/data/sites/16/d2. 2. pdf). Of course the users do not know which of the data points are part of the gold standard dataset. This kind of quality control test could have been addressed by the authors. Additionally, the authors do not mention if they have any system of profile management: eg, for reducing the weight of votes from a user who always votes against the majority, or for taking into account the relative experience of each user. Simple majority vote is not always the best choice to resolve disagreements, especially for a task like systematic review, where one expert vote can be more valuable than a number of nonexpert votes. Also, it is not clear if there are any strategies to tackle possible conflict of interest from the voters. Another point to raise is the difficulty of recruiting contributors, as the authors state in their Limitations section.
DATA Systematic reviews are included in the system if they match two criteria: have the words "systematic review" or "meta-analysis" in the title and include at least one link to a trial in the ClinicalTrials. gov registry. The authors deliberately chose this "conservative" approach, but we would like to point out that there are alternative approaches showing operational performance, eg, the rule-based algorithm proposed by Sarker, and Diego Moll a-Aliod 5 is reported to achieve the recall of 0. 99 and precision of 1 for meta-analysis and systematic reviews. Adopting a similar approach would widen the selection of included reviews. We would also like to raise the question of including systematic reviews that are not in open access. The researchers working on them might be interested in using the proposed tool, but it was not very clear to us from the article whether it is possible to include such a review in the system and how it is managed.
Another questionable choice is including the trial registration entries from ClinicalTrials. gov only. There is a number of other trial registries (see eg, https://www. hhs. gov/ohrp/international/clinicaltrial-registries/index. html), as well as the WHO portal which provides access to a few primary registries. Including only one registry apparently limits the included trials. The obvious reason for using ClinicalTrials. gov is the fact that metadata of articles in PubMed can contain a direct link to it, while links to other registries are not included in the metadata. However, trial registration numbers are often cited in the abstract and follow a fixed pattern including the registry name and the unique registration ID, which can be easily found with the help of regular expressions and used to automatically find the registry entry on the webpage of the corresponding registry.
ACKNOWLEDGMENTS This work is a result of a Journal Club organised within the  Methods in Research on Research (MiRoR)  project. Special thanks to  Christopher Norman ,  Melissa Sharp  and  David Blanco  for their comments and suggestions. We would also like to thank the members of the  Methods in Research on Research (MiRoR) Project  (http://miror-ejd. eu/) and  Marie Sklodowska-Curie Actions  for their support.
Methods The present study is a secondary analysis of the database collected for the GLIMP study, an international, multicenter, point-prevalence study of hospitalized adult patients with a diagnosis of CAP  [12]. Detailed methodology of the GLIMP study was published elsewhere  [12]. The study was conducted in accordance with the amended Declaration of Helsinki and was approved by the Institutional Review Board (IRB# HSC20150184E) of The University of Texas Health Science Center at San Antonio, TX, USA, and all participating centers were required to comply with local, regional, or national research regulations to participate in the study.
Data collection Study participants were enrolled on a single day in the months of March, April, May, and June 2015. The following variables were collected: age, height, weight, gender, job, smoking history, pharmacological therapy, vaccination status, drug and alcohol abuse, oncological, cardiovascular, respiratory, hepatic, and renal comorbidities, previous healthcare exposure -i. e. emergency room admission, intravenous and oral antibiotics, hospitalization, lower respiratory tract infections in the previous 3, 6, and 12 months -severity of disease in first 24 h of hospital admission, prior infection or colonisation with multi-drug resistant pathogens. For a detailed list of characteristics and risk factors evaluated please see the Appendix A. Patients' care workup might include any of the following specimens: blood samples, acute-phase serum specimens, urine samples, nasopharyngeal swabs, sputum in case of productive cough, pleural fluid, endotracheal aspirates, and bronchoalveolar lavage samples.
Statistical analysis The frequency of viral nasopharyngeal swab tests was calculated considering all the CAP patients included in the GLIMP dataset. The prevalence of viral CAP was calculated using viral isolates detected with viral nasopharyngeal swabs performed during the first 24 h of hospital admission. Categorical variables, expressed as counts (percentages), were compared between groups using the Chi-squared or Fisher test, when appropriate. Regressions analyses were performed to compare prevalence and determine odds ratios (OR) with 95% confidence interval (CI). Logistic regression analyses were performed to assess the relationship between viral pneumonia, influenza virus pneumonia and demographics, therapeutic, epidemiological, and clinical variables. The Chi-squared test was performed to compare the prevalence between countries and continents.
The study period may also have influenced the prevalence of viral testing, especially for influenza in the northern hemisphere. Furthermore, the present study did not evaluate outcomes of patients treated with oseltamivir in comparison with those who did not receive therapy. Finally, viral identification was assessed by local protocol and not per study guidelines. In fact, compared with other participating centers, North America had the highest prevalence of Rhinovirus/Enterovirus pneumonia. This is line with data from the EPIC study  [2] , although it may be explained by different PCR sensitivity which increased the diagnostic yield for those specific pathogens  [47]. We acknowledge that the pragmatic approach of the study represents an important limitation: microbiological sampling and patients' management depend on local standard procedures and not by a study protocol.
This two-step study evaluated the cost-effectiveness of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) for presurgery staging of non-small cell lung cancer (NSCLC) in France (EVIEPEB; ClinicalTrial. gov identifier NCT00960271). Step 1 consisted of a high-benchmark EBUS-TBNA-training program in participating hospital centers. Step 2 was a prospective, national, multicenter study on patients with confirmed or suspected NSCLC and an indication for mediastinal staging with at least one lymph node > 1 cm in diameter. Patients with negative or uninformative EBUS-TBNA and positron-emission tomography-positive or -negative nodes, respectively, underwent either mediastinoscopy or surgery. Direct costs related to final diagnosis of node status were prospectively recorded.