dataesr/scientific-paragraphs-categorization huggingface dataset:
Authorship contributions Conception of the study by HJS and EAA. The design of this study was generated primarily by HJS, EAA, MC, MB, QZ, SN, FM, GG, SM, DG, GL, MDN, AI, GG, and LK. HJS, MV, SN, FM, GA, GG, GL, WA, GB, HB, BL, RL, CL, RM, KS, AM, UP, JP, and CK worked to facilitate the data sharing process of at least one of the eligible clinical trials. MV, QZ, TB, LM, IDF, ZS, and OGA extracted relevant data from at least one shared clinical trial into a unified database. QZ performed statistical analysis. HJS, MC, MB, QZ, SN, FM, GG, DG, GL, LK, MDN, AI, IN, MBS, EAA, and MV interpreted the data and HJS and MV drafted the manuscript. All authors revised the manuscript for important intellectual content and approved the final version of the manuscript.
| Study design and participants This was a multicentre, observational study conducted in 10 countries in Africa (Algeria [182 paediatricians], Morocco [203], Tunisia [50], Mauritius [28], Madagascar [38], Senegal [15], Gabon [35], Congo [39], Ivory Coast [107] and Cameroon [62]). As a non-interventional study, the visits and procedures were the community standard practice, and there were no procedures other than usual patient management and collection of specific data by questionnaires. The questionnaire was developed in consultation with African and international experts and was designed to be as clear as possible regarding the terms and language understood and used by African paediatricians. Questionnaires were completed by the treating paediatricians following consultation of full-term, and otherwise healthy infants (per clinical examination of the paediatrician) between 0 and 12 months of age presenting for the first time with functional gastrointestinal symptoms as the main complaint, and who were not known to be allergic to cow's milk protein.
Paediatricians were instructed about FGIDs in advance of the study and questionnaires included definitions for 3 FGIDs (infant colic, constipation and regurgitation) and 1 symptom (gas/bloating). The questionnaires were used to collect data on professional practice experience of each in this patient population, and on the individual patient cases, including a description and characteristics of the FGID or gas/bloating by the paediatrician and the first intent patient management prescribed. The questionnaires were completed from June to November 2017.
| Statistical analyses Continuous variables were described by the number of observations, the mean, standard deviation, median, inter-quartile range, minimum and maximum. Categorical variables were described using the frequency of infants by category. Missing data were not replaced and were not imputed for inclusion in the data analyses. An arbitrary sample size of 10 000 infants was targeted, to be included by approximately 900 paediatricians with each paediatrician having a 2-month period to include 12 infants. Statistical comparisons were performed using two-sided chi-square tests with a type I error and a significance level of α =. 05. All statistical analyses were performed by Groupe Axonal-Biostatem, France, using SAS ® version 9. 4.
16 It can be hypothesised that prescribing medication  for infants is used at least in part as an effort to manage parental anxiety. However, the prescription of medication in this way is not only unnecessary and has no supportive evidence, but can also be associated with side effects and can have an impact on healthcare expenditure. 27 mitations of this study included that participating paediatricians were not selected using a specific methodology and therefore may not be fully representative of the wider population. Only questions regarding regurgitation, infant colic and functional constipation as the putatively most common FGIDs were included. Other FGIDS, such as functional diarrhoea or dyschezia, were not evaluated to avoid the risk of misdiagnosis of these less common symptoms, which could have introduced the risk of less accurate data into the analyses.
This approach served to foster compliance with the study procedures and therefore maximise the robustness of the data collected. Also, although there was an adverse impact of regurgitation, infant colic, functional constipation and gas/bloating on infant quality of life, quality of sleep and parental anxiety, no comparator group was included to provide information on these criteria in otherwise healthy infants nor the prevalence in the general paediatric population. The type of prescribed medications (eg the types of antispasmodic, laxative, prokinetic and antacid) was not recorded. A further potential limitation was that most infants were from urban areas since areas of higher literacy were preferred. Data on feeding type are not evaluated since the study was not designed for this comparison and the populations were not balanced in this regard.
Statistical Analysis We used IBM SPSS software version 20. 0 (IBM Corp. , Armonk, NY, USA) to carry statistical analyses and express the results for quantitative parameters as mean ± SD. Differences among the groups were searched by one-way analysis of variance using Bonferroni correction for multiple comparisons with post hoc tests. Analysis of qualitative variables was used chi-square tests. Logistic regression was used for data analysis that systematically included IBS as the dependent variable and as independent variables, BMI, and the validity and clinical MMPI-2 scales. For the analysis of the psychological characteristics of the IBS subtypes, 2 multinomial logistic regression models, adjusted for BMI, with the non-IBS and the unspecified IBS subtype used as reference groups, were created for each group.
To avoid any bias, we excluded from the study patients unable to respond to the entire test (5% of FGIDs patients) and patients with organic diseases. Data are based on the other 608 patients. ] [29] [30]  In previous clinical studies, IBS patients were compared to healthy controls 28 or in patients with extra digestive pain. 7 he present study uses other FGIDs patients as the control group for 2 reasons. First, MMPI scales used are T scores with a known distribution in the healthy population (mean ± SD, 50 ± 10), according to the gender and age; second, other FGID patients suffer from another chronic digestive disorder. The main superiority of the MMPI on other psychological tests is the presence of validity scales to validate the questionnaires. 31 evertheless, the high correlation between the MMPI clinical scales limits the power of this test.
The present results could not be predictive of similar findings in a community population. The cause-effect relationship between abnormal psychological traits and IBS symptoms remains controversial. Our data and other reports illustrate that abnormal psychological profiles do exist in IBS patients seeking medical help. 40 On the other hand, Drossman et al. 13  reported that psychological profiles in IBS nonpatients and subjects showing IBS symptoms but never seeking medical help were not different from the psychological profiles of healthy controls. From these observations, it was proposed that abnormal psychological behaviors are closely related to IBS patients seeking medical help. However, the control population of our study was composed of FGIDs patients with the same history of chronic functional digestive disease.
The study was conducted in the surgical departments of 40 hospitals across France (supplementary figure  S1 ). We screened eligible departments in all public and private hospitals performing digestive tract surgery and combined the criteria related to volume of inpatient stays (≥600 per year), rate of major adverse events (≥3. 5%), and data coding quality (≥2 secondary diagnoses on average for each hospital stay). Among 134 eligible surgical departments, we enrolled the first 40 responders. All adults who underwent one of the following operative procedures in those departments were considered for inclusion: hernia repair, cholecystectomy, appendectomy, bariatric, colorectal, hepatopancreatic, or oesophageal and gastric surgery (see operations and procedures codes in supplementary appendix).
Compliance with those items was mainly determined by external review of information recorded in the logbook, in addition to pictures of team meetings or posters in operating rooms and formal presentation of the improvement plan by on-site leaders during training sessions. We obtained data from the French Medical Information System, Programme de Médicalisation des Systèmes d'Information (PMSI). The PMSI is a nationwide database routinely used for care reimbursement and updated weekly with data from all hospitals in France. The data are prospectively collected, and the database relies on a coding system with strict definitions for variables. A subset of records is audited regularly to avoid coding errors. Because of the accuracy and exhaustive data collection of the PMSI database, no patients were lost to follow-up during the study period.
Inpatient stays were recorded as standard discharge abstracts containing compulsory information about patients and their primary or secondary diagnoses using ICD-10 (international classification of diseases, 10th revision) codes as well as detailed procedural codes associated with the care provided. From the PMSI database, we extracted the personal characteristics of the patients; comorbidities according to the Elixhauser algorithm, which has acceptable validity  14  ; emergency admission; date and type of operative procedure; main diagnosis; complexity of the surgical procedure; and median household income based on residential codes.
The models were adjusted for potential confounders from a patient risk score that predicted the probability of adverse events and was previously developed from patient data derived during the randomisation period (2016). We established a specific risk score separately for each outcome and operative procedure considering all variables extracted from PMSI and hospital status in multivariable logistic regression. In the secondary analyses, we first split the intervention hospitals into a high compliance group and a moderate to poor compliance group to investigate whether compliance with the control chart implementation programme was associated with better outcomes. Then we evaluated the impact of the intervention on signal frequency for detection of variation in special causes, considering all control charts of participating hospitals whatever the outcome and procedure type.
Deterioration or improvement in surgical outcomes was counted as the number of upward or downward signals, and this was modelled using mixed effect Poisson regression models with ratios of rate ratios estimations. Sensitivity analyses included patients who underwent ambulatory surgery. Operative procedures were stratified into minor (hernia, cholecystectomy, appendectomy, bariatric) and major (colorectal, hepatopancreatic, oesophageal and gastric) surgery. Missing household incomes in the dataset were imputed based on the mean value for each study group and period. Analysts were not blinded as a result of the modelling of intervention impact according to its level of implementation, which deliberately introduced a distinction between surgical departments among study groups. All reported P values were two sided and we considered a value of less than 0.
32 Because that information is critical for billing purposes, inpatient deaths, intensive Odds ratios were estimated using mixed effect logistic regression models to compare surgical outcomes between pre-implementation and implementation periods in intervention and control hospitals. Ratio of odds ratios captured the impact of the control chart by comparing the change in outcomes from the pre-implementation to implementation periods between the intervention and control hospitals based on a difference-in-differences approach. A ratio of odds ratios value less than unity indicated improvement as a result of control charts in intervention versus control hospitals. Estimates with corresponding 95% confidence interval considered clustering of patients at the hospital level. care stays, and reoperations are accurately collected in hospitals claims databases.
Data After their regular appointment, GPs interviewed participating patients for the purposes of the study. Only patients diagnosed by the GP with a work-related CMD in the preceding 12 months or at the time of the appointment (depressive disorder, anxiety disorder or alcohol abuse) were included in the analyses. Collected data were: Patient management in the preceding 6 months or at the time of the appointment (completed by the GP): Psychological support; Prescription of psychotropic medication (antidepressants, anxiolytics and hypnotics); Referral to a psychologist/psychiatrist; Sick leaves prescription; Referral to OP.
Acknowledgements The authors thank all the participating GPs of the  Nord-Pas-de-Calais region  and their patients who participated to the Héraclès study. The authors thank the department of general practice of Lille's University and the regional union of health professional of GP's (URPS-ML) of the  Nord-Pas-de-Calais region  for their involvement in the GP recruitment phase. The authors also want to thank the Héraclès study scientific committee members who contributed to the brainstorming and the set-up of this survey. Santé Publique, IPLESP, 75012 Paris, France. 2  Department of Infectious Diseases ,  Réseau Sentinelles ,  Centre Hospitalier Régional , d'Orléans 14 avenue de l'hôpital, 45000 Orléans, France. 3 University department of general practice,  Université Lille 2, Lille, France.
It is undeniable that collecting data from several database sources (healthcare-system insurance, hospital data, smart sensors, mobile apps, and more) and cross-referencing the data, even if it has been anonymized, will necessarily facilitate patient re-identification  [28, 29]. Note too that in a context where the French healthcare insurance system is pushing hard to promote the electronic health records (French acronym DMP) system, these concerns connect to the fact that only one in three patients accept letting the health insurance system hold and store their health data  [30]. On the physicians side of the equation, Mathieu-Fritz  [31, 32]  has elegantly illustrated how electronic health records fuel tension between data transmission and preserving physician-patient privilege, and this tension was found to pollute practice in a study on e-record rollout in a region of northernmost France.
That said, more than 5 years have passed since Mathieu-Fritz's research, and both the DMP electronic health record and the allied digital service practices have since evolved. The inter-insurer national healthcare claims information system (French acronym SNIIRAM) is the world's biggest healthcare database, hosting more than 1. 2 billion care flow sheets, 500 million medical procedures, and 15 million hospital stays every year. Policymakers and health professionals alike both need this data to inform and adopt healthcare system reforms, and the health industry is proposing new solutions and a long-term contracts-driven vision. PLHIV and their physicians need further clarifications on the current protections before they can gauge whether or not the legal protections in place need to be strengthened.
Cases Cases came from the French National Confidential Enquiry into Maternal Deaths (ENCMM, Enquête Confidentielle sur les Morts Maternelles)  [16, 17]. This permanent survey has studied all deaths since 1996 of women during pregnancy or within 1 year of its end. The sources used to identify these deaths and the procedures for investigating and assessing them have been described in detail  [16, 18]. In brief, besides spontaneous notifications by clinicians, deaths are identified from three sources: (1) death certificates of women of reproductive age; (2) computer-based national linkage of the death register with the birth register, from death and birth certificates; and (3) hospital discharge databases. Two assessors (an obstetrician or midwife and an anaesthetist) conduct a confidential enquiry of each pregnancy-associated death identified, collecting relevant clinical information about the woman and her death through interviews and review of the medical records and autopsy reports.
Controls Controls came from a representative sample of births in France, the 2010 French National Perinatal Survey (NPS), a repeated cross-sectional study conducted periodically since 1995 to produce statistics about perinatal indicators  [19]. These surveys cover all live-and stillbirths at or after 22 weeks of gestation or with a birth weight of at least 500 g in every maternity unit in France for a complete 1-week period (1/52nd of births in France). Midwives interview mothers before discharge to obtain data on maternal social and demographic characteristics and prenatal care. Data about the pregnancy, delivery and newborns are collected from medical records. The 2010 version of the NPS, in the middle of the 2007-2012 case inclusion period, served as the source population for the control women in this analysis (n = 14 681).
BMI was calculated from self-reported prepregnancy weight and height in the medical records of cases and controls. BMI categories for class 2 and class 3 obesity were combined because of the small number of cases in these groups. The precise BMI for some women was not available because either weight or height or both were missing, but the prepregnancy obesity status (yes/no) was reported from another questionnaire item. The following covariates were examined: mother's country of birth (France or other European country, North Africa, sub-Saharan Africa, other), mother's age (≤24, 25-29, 30-34, 35-39, ≥40 years), educational level of zip-code of residence (proportion of adults 20-45 years who did not complete high school, grouped into quintiles based on data available from the French census) as a proxy for socioeconomic status, severe pre-existing chronic conditions (a composite binary variable including hypertension, diabetes, cardiac diseases, and other notable chronic conditions), previous deliveries (nulliparous, only vaginal deliveries, one or more caesarean deliveries), multiple pregnancy and median gestational age (weeks).
Crude and adjusted odds ratios were calculated with 95% confidence intervals. Interactions between obesity and covariables were systematically tested. The proportion of women with missing information was 15. 9% for obesity status and 33. 0% for BMI among maternal deaths, and 7. 0 and 7. 5% respectively among controls. We handled missing values by multiple imputation with chained equations (10 datasets) on the missing-atrandom hypothesis  [21]. The multiple imputation prediction model was composed of the outcome variable, prepregnancy BMI, maternal age, country of birth, quintile of zip-code education level, pre-existing chronic conditions, previous deliveries, multiple pregnancy and duration of pregnancy. Results are presented with imputed data. Post-hoc calculations showed that with a significance level of 5% and obesity prevalence of 10% among controls, this sample would have >90% power to detect a doubled risk of death in obese versus non-obese women (OR ≥ 2.
0). We performed several sensitivity analyses. The first tested the impact of adjusting for severe chronic conditions before pregnancy together with confounders in the multivariate logistic regression model; although we primarily considered this variable an intermediate factor (see above), it might also affect maternal risk directly, regardless of obesity status. Second, we assessed the relevance of the multiple imputation for missing data and performed an analysis of the association between BMI and maternal mortality with non-imputed data. The second part of the analysis was restricted to women who died. Here we compared the proportion of suboptimal care, assessed by the ENCMM expert committee, by obesity status. To isolate the components of suboptimal care directly related to obesity, we reviewed each death of an obese woman with suboptimal care, relying on the ENCMM committee determinations given the absence of specific clinical guidelines in France for managing pregnancy in women with obesity.
Limitations This study has some limitations. First, some maternal BMI data were missing. Nonetheless, we were able to include all women in the analysis by using multiple imputation for missing data, and the results of the sensitivity analysis with non-imputed data were similar to those with imputed data. Another limitation, due to case number constraints, was the need to study obesity with a binary variable, rather than classes of BMI, in the risk assessment by specific causes of maternal mortality. Because this binary obesity variable combines women with underweight, normal weight, and overweight women in the same reference class, it may result in underestimating the association studied, but does not challenge our main conclusions. Additionally, prepregnancy BMI may be inaccurate because height and weight were self-reported.
Acknowledgements * Monica Saucedo monica. saucedo@inserm. fr Acknowledgements We thank the obstetricians, midwives, and anaesthetists of the  French Confidential Enquiry into Maternal Deaths  who collected information for the case reviews and the national centre for cause-of-death statistics and epidemiology (CépiDc) for assistance in case identification. We also acknowledge the team and all the participants of the  French National Perinatal Survey. We also thank  Jo Ann Cahn  for editorial assistance. CNEMM study group Dominique Chassard 6 ,  Henri Cohen  7 , Michel Dreyfus 8 , Jean-Claude Ducloy 9 ,  Irina Guseva-Canu 10  ,  Jean-Pierre Laplace   11  ,  Véronique Le Guern 12  ,  Sylvie Leroux 13  ,  Estelle Morau 14  ,  Claire Rondet 15  ,  Mathias Rossignol 16  ,  Véronique Tessier  17 , Éric Verspyck 18 , Philippe Weber 19 , Laurent Zieleskiewicz 20 6  Department of Anesthesia and Intensive Care, Femme Mère Enfant Hospital, Hospices Civiles de Lyon, Lyon, France ; 7  Department of Obstetrics and Gynecology, Institut Mutuliste Montsouris, Paris, France ; 8  Department of Obstetrics and Gynecology, CHU de Caen, Caen , France; 9  Department of Anesthesia, Hôpital privé Villeneuve d'Ascq, Villeneuve d'Ascq , France; 10 Non-communicable diseases and trauma division, Santé publique France, Saint-Maurice, France; 11  Department of Obstetrics and Gynecology, Polyclinique Bordeaux Nord Aquitaine, Bordeaux, France ;  12   Centre de Référence Maladies Auto-Immunes et Systémiques Rares ,  Service de Médecine Interne Pôle Médecine, Hôpital Cochin, AP-HP, Paris, France ; 13  Department of Obstetrics and Gynecology, Centre Hospitalier Annecy-Genevois, Annecy , France; 14  Department of Anaesthesia, CHU de Nimes, Nimes, France; 15 Centre for Epidemiology on Medical Causes of Death (CépiDc-Inserm ),  Le Kremlin-Bicêtre , France; 16  Department of Intensive Care, Hôpital Lariboisière, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France ;  17   DHU Risques et Grossesse, Maternité Port Royal, Hôpitaux Universitaires Paris Centre, Hôpital Cochin, AP-HP, Paris , France; 18  Department of Obstetrics and Gynecology ,  CHU de Rouen, Rouen, France ; 19  Department of Obstetrics and Gynecology, Centre Hospitalier de Mulhouse, Mulhouse, France; 20 Département Anesthésie-Réanimation, Assistance publique-Hôpitaux de Marseille, Hôpital Nord, Marseille, France.