dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to all the participants who accepted to participate in this study. C. V. was supported by a grant of the  Università Italo-Francese/Université Franco-Italienne , the  Zegna Founder's Scholarship  and  Associazione Amici di Claudio Demattè. F. P. , A. F. and V. G. were supported by a grant of the  Agence Nationale de la Recherche  ( ANR-16-CE17-0016 ,  VIRTUALHEARING3D , France), by a  prize of the Foundation Medisite (France) , by the  Neurodis Foundation (France)  and by a grant from the  Italian Ministry for Research and University (MUR ,  PRIN 20177894ZH ). The study was supported by the  IHU  CaSaMe  ANR-10-UBHU-0003  and  ANR 2019CE37  Blind Touch. We thank  J. L. Borach  and  S. Alouche  for administrative support,  E. Koun  for informatics support and  Giordana Torresani  for graphical support (Figs.
Methods In a randomised, double blind, two-parallel group, placebo-controlled trial (NCT02990286), patients with connective tissue disease-associated ILD or idiopathic interstitial pneumonia (with or without autoimmune feature) and a NSIP pattern (defined on NSIP pathological pattern or on integration of clinico-biological data and a NSIP-like HRCT pattern) were randomly assigned in a 1:1 ratio to receive rituximab (1000 mg) or placebo on day 1 and day 15 in addition to MMF (2 g daily) for six months. The primary endpoint was the change in percent of predicted forced vital capacity (FVC) from baseline to 6 months analysed by a linear mixed model for repeated measures analysis. Secondary endpoints included progression-free survival (PFS) up to 6 months and safety.
Trial design This was a multicentre, randomised, double blind, two-parallel group, placebo-controlled, superiority trial. The methods of this study have been previously described  [13]  (appendix pp23-89). The trial was conducted at 17 academic French centres specialised in rare pulmonary diseases including ILDs, all being part of the OrphaLung and RespiFIL (Filière Santé Maladies Respiratoires Rares) networks. All trial aspects were conducted in accordance with the ethical principles of the Good Clinical Practice guidelines and the Declaration of Helsinki, in compliance with French laws. This trial is registered with ClinicalTrials. gov (NCT02990286).
We used two-sided significance tests with a type I error of 5%. For the primary analysis, we used a linear mixed model for repeated measures to compare differences between study groups in the slope of FVC measurements over the 6 months study period (time points at baseline, 3 and 6 months). The model included treatment group, visit and treatment-by-visit interaction as fixed-effects. We used a model with random individual intercepts and slopes over time. The treatment effect was assessed using the treatment-by-visit interaction. This model assumes data are missing at random, and missing data were not imputed for the primary analysis. For patients who did not attend all the study visits, we used all available FVC measurements in the primary analysis. We performed different imputation sensitivity analyses to assess the impact of missing data and the robustness of the treatment effect under different assumptions for missing data (appendix Table  S8   A priori subgroups were defined by stratification variables for randomisation: ILD groups (CTD-ILD or IPAF vs.
iILD) and FVC in % predicted value at inclusion (< 50% vs. ≥ 50%). We also performed post hoc exploratory subgroup analyses (appendix pp 7) For secondary efficacy endpoints, all data from baseline to 6 months were used without imputation of values for patients who discontinued early. We reported p values with no adjustment for multiplicity and for descriptive purposes only. The predicted change in FVC in ml between baseline and 6 months was analysed using the same method as for the primary endpoint. Progression-free survival was described by Kaplan-Meier curves and compared between study groups using a log-rank test and unadjusted Cox proportional hazards model. A Cox proportional hazards model with post-hoc adjustment on stratification variables was also fitted. Between-group comparisons for absolute changes between baseline and 6 months in continuous outcomes were performed with Student's t-tests.
Acknowledgments We gratefully acknowledge the patients for their participation in this trial. This study was supported by a grant from the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique ) 2015. The trial protocol was written with support from a Grand-Ouest interregion support project aiming to promote and facilitate clinical research on monoclonal antibodies (MIAMIGO) and from the  Pilot Centre for Therapeutic Antibodies Monitoring (PITAM-CePiBAc) of Tours University Hospital. We thank  Estelle Boivin ,  Mathilde Husson ,  Elody Mureau  and  Magali Rehaut  for their contribution in this study. The authors would like to thank the Research Coordinators from the  Clinical Investigation Center of Lyon, Inserm.
ABSTRACT Introduction Considering the high prevalence of polypharmacy in pregnant women and the knowledge gap in the risk-benefit safety profile of their often-complex treatment plan, more research is needed to optimise prescribing. In this study, we aim to detect adverse and protective effect signals of exposure to individual and pairwise combinations of medications during pregnancy. Methods and analysis Using a range of realworld data sources from the UK, we aim to conduct a pharmacovigilance study to assess the safety of medications prescribed during the preconception period (3 months prior to conception) and first trimester of pregnancy. Women aged between 15 and 49 years with a record of pregnancy within the Clinical Practice Research Datalink (CPRD) Pregnancy Register, the Welsh Secure Anonymised Information Linkage (SAIL), the Scottish Morbidity Record (SMR) data sets and the Northern Ireland Maternity System (NIMATS) will be included.
The feasibility of mining RWD to detect adverse and protective effect signals of individual medications during pregnancy has already been established. 16 18  In this study, as part of the 'Multimorbidity and Pregnancy: Determinants, Clusters, Consequences and Trajectories (MuM-PreDiCT)' consortium, we aim to develop and implement a systematic signal detection methodology to determine adverse and protective signals from both individual and combinatorial medication exposure on the incidence of various pregnancy outcomes, using a set of large primary care, secondary care and maternity care databases within the UK.
AIMS The study aims to evaluate the feasibility of conducting a signal detection study estimating and evaluating the adverse and protective signals of medications prescribed during the preconception period and the first trimester of pregnancy on the incidence of various pregnancy outcomes using RWD. The key objectives include the following: 1. To scope RWD sources in UK, and to check their feasibility for signal detection by exploring the range of medications prescribed and recorded within said data sources during the preconception period and first trimester of pregnancy. 2. To identify a core list of pregnancy outcomes, and to explore the feasibility of capturing these outcomes within said data sources 3. To apply suitably developed methods from published literature to estimate measures of disproportionality for each of the exposure-outcome pairs and detect signals.
METHODS AND ANALYSIS Data source Four population-based data sources spanning all four nations of the UK will provide data for this signal detection CPRD Gold contains data on medications prescribed within primary care with associated prescription time stamps, encoded using drug codes that are assigned and categorised according to British National Formulary (BNF) item codes. However, CPRD Gold may be limited by their unavailability of secondary care and over the counter medication data, and data on whether these prescriptions were dispensed. Within CPRD, the CPRD Pregnancy Register is an algorithm that takes information from maternity, antenatal and birth health records from primary care to detect Yes, using prescription records on the GP system. This is coded using the Dictionary of Medicines and Devices (DM+D).
The secure anonymised information linkage The SAIL databank, a population level database in Wales, is a repository of anonymised health and socioeconomic administrative data that provide linkage at an individual level. 22  It holds health data for 4. 8 million people and includes data contributed by 80% of Welsh general practices. National Community Child Health Data set, GP records and hospital records have been used to detect pregnancies in SAIL. 23  In addition, patient level linkage to the Welsh Longitudinal General Practice data set and the Welsh Demographic Service data set has been used to obtain data on diagnoses, prescriptions and demographics data, respectively. The Welsh Dispensing Data.
Study population For this feasibility study, we aim to conduct the analyses separately across all four databases described in the section above. Women aged between 15 and 49 years with a pregnancy recorded within the CPRD Gold Pregnancy Register (all four nations), National Community Child Health data (Wales), SMR02 (Scotland) and NIMATS Yes, using prescription from the community. This is coded using the Dictionary of Medicines and Devices (DM+D). Each product within the dictionary is associated with a BNF code to which the product belongs. The range of exposures for this study will be defined using the BNF code. Limitation: the data relate to primary care prescribing only. Secondary care prescribing data, over the counter medications and data on adherence to treatments are unavailable using this data source Feasibility to identify pregnancy outcomes? Only secondary care Open access (Northern Ireland), within the study period customised to the period of data availability within each data source will be eligible for inclusion in this study (tables 1-4).
Outcomes The MuM-PreDiCT and ConcePTION consortium have developed core outcome sets (a minimum set of recommended maternal and offspring outcomes) for studies of pregnant women with multiple long-term conditions and for studies generating medication safety evidence, respectively. 25 26  The outcome set was reviewed by a study advisory group panel comprising of GPs, obstetricians, obstetric physicians and experienced users of the available data sources, to identify the pregnancy outcomes' suitability and feasibility for inclusion in this signal detection study. The availability, prevalence, quality and completeness of data recording of these outcomes within the said data sources were used as criteria to determine the feasibility of the outcome of interest. Outcomes with poor recording in a specific database may not be included in the analysis using that database to avoid noisy signals due to insufficient power or case misclassification.
Exposure All medications prescribed within primary care (as in the CPRD Gold database (all four nations) and EPD (Northern Ireland)) and all medications dispensed in the community (as in the PIS database (Scotland)) have a BNF code associated with them in the UK. Dispensed medications in WDDS (Wales) are coded based on the Dictionary of Medicines and Devices (DM+D). Using a complete extract of the dictionary, 27 each dispensed item within the dictionary can be mapped to a corresponding BNF code. Analysis stratified by BNF codes has been established in a previous analysis, where using a dictionary of medications prescribed within primary care, we stratified the medications prescribed according to their 4-digit BNF code (BNF chapter, section, paragraph and subparagraph), screened and selected 577 BNF items that were pharmacological agents with therapeutic action.
Covariates The following demographic and health characteristics will be obtained from the four data sets; maternal age at the start of pregnancy, ethnicity, smoking status as recorded prior to the start of pregnancy, pregravid body mass index (BMI) and a wide range of comorbidities. Patients with missing data on smoking status, pregravid BMI and ethnicity will be categorised into a separate missing category within the corresponding variable. The list of 79 pre-existing long-term comorbidities for which baseline data were extracted, and their definitions is presented in a previous publication. 23  Ethnicity will be categorised as White, South Asian, Black Afro-Caribbean, mixed ethnic background and other ethnic minority groups including Chinese. Latest BMI recorded prior to the start of pregnancy will be considered as pregravid BMI, and will be categorised according to the WHO definition as underweight (<18.
Relative decrease in p-value (p-RD) prior to and after adjustment for covariates will be calculated using the formula below. 16  p -RD = p ( before adj for DRS ) -p ( after adj for DRS ) p ( before adjustment ) In addition to statistical measures of disproportionality, descriptive data stratified by their outcome status will be presented. These include numbers and proportions of eligible pregnancies with a prescription of the individual medication or medication combinations (pairs) during the two separate exposure windows. Benjamini-Hochberg correction for multiple testing will be applied with a threshold of 0. 20. The analyses in each of the four data sets will be conducted separately. Signals arising from each of the four data sources will be reviewed side-by-side through a systematic signal review workshop described below.
Funding This work is independent research funded by the  Strategic Priority Fund 'Tackling multimorbidity at scale' programme  (grant number  MR/W014432/1 ) delivered by the  Medical Research Council  and the  National Institute for Health Research  in partnership with the  Economic and Social Research Council  and in collaboration with the  Engineering and Physical Sciences Research Council. The views expressed are those of the author and not necessarily those of the funders, the  NIHR  or the  UK Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work was also supported by  Health Data Research UK  ( HDRUK2023. 0030 ), which is funded by  UK Research and Innovation , the  Medical Research Council , the  British Heart Foundation ,  Cancer Research UK , the  National Institute for Health and Care Research , the  Economic and Social Research Council , the  Engineering and Physical Sciences Research Council, Health and Care Research Wales, Health and Social Care Research and Development Division (Public Health Agency, Northern Ireland) ,  Chief Scientist Office of the Scottish Government Health and Social Care Directorates.
(OPDW): attendance information for all NHS Wales hospital outpatient appointments. ► (NCCHD): comprises information pertaining to birth registration, monitoring of child health examinations and immunisations. ► (MIDS): contains data relating to the woman at initial assessment and to the mother and baby (or babies) for all births. ► (WDDS): containing information on GP prescribed medications, dispensed medications by community contractors have been linked to the SAIL databank. ► (WDSD): used to extract Lower-layer Super Output Area (LSOA) version 2011 information associated with area-level deprivation (the Welsh Index for Multiple Deprivation (WIMD) version 2019). ► All births in Wales collected from birth registrations. ► All deaths relating to Welsh residents, including those that died out of Wales.
Information about fetuses or babies who has or is suspected of having a congenital anomaly, Geographical region Wales Coverage The database contains 90% of all the GP data in Wales and 100% of all hospital admissions (PEDW) Linkage availability to ► Primary care data (from Wales Longitudinal General Practice (WLGP)). ► Secondary care data (from inpatient hospital admissions, inpatient from Patient Episode Database for Wales (PEDW) and outpatient from Outpatient Database for Wales (OPDW). ► The National Community Child Health (NCCH). ► The Maternal Indicators (MIDS). ► The Welsh Dispensing Data Set (WDDS). ► Period of data availability 2000-2022 Feasibility to identify Yes, using GP, PEDW, NCCHD and MIDS data sets pregnancy episodes? Sample size-number of ~475 000 pregnancies (from the year 2000 onwards) pregnancy episodes.
1136/bmjopen-2023-073162 on 9 October 2023. Downloaded from Specific major congenital anomalies (not including termination of pregnancy due to fetal anomaly) Maternal death Additional outcomes from MuM-PreDiCT consortium Maternal outcomes Clinical: antenatal Pre-eclampsia, eclampsia, HELLP syndrome, gestational hypertension Congenital anomalies recorded with sufficient quality will be included. Data available in primary care data, and linkage to baby records identified from MBL data Data available in primary care data Data available in primary care and linked HES data inform it. Data available in CARIS database Data available from GP/ Patient Episode Data for Wales (PEDW)/ADDE for death during childbirth Data available in primary care and linked PEDW data (if diagnosed) Data unavailable. Data in hospital discharge records are not suitable for identifying specific congenital anomalies.
To accomplish this, the identification of GBCA use and the estimation of Gd anth fluxes into the ocean appear to be key tools for conducting a large-scale assessment of Gd anth discharge. Therefore, for the first time, our study aims to propose an annual Gd anth flux model for 48 European countries. This flux model will be based on GBCA consumption data from France and thus extrapolated to the European seaboards (Atlantic Ocean, Mediterranean Sea, Baltic Sea, Black Sea). Our study aims to identify the main current contributors and the countries likely to become contributors to the European Gd anth flux in the future. Finally, special attention will be paid to the year 2020, in order to determine whether a major health crisis, like the one caused by COVID-19, can affect the transport of these emerging contaminants to the ocean.
Flux model based on GBCA consumption Since 2014, data on medication intake for France has been available, making it possible to obtain GBCA consumption by French region (Supplementary Table  A. 1), as well as patient age category and gender (NC i ; Open Medic database, 2022). The GBCA package leaflets provide information on the medication's Gd concentrations ([Gd] medication i ) and the volume injected into patients (V i ) based on their body weight (BDPM, 2022). Six GBCA formulations (i) were distributed in France between 2014 and 2020: gadopentetic acid; gadoteric acid; gadodiamide; gadoteridol, gadobenic acid and gadobutrol. In the flux model, gadoteric acid was also considered in its formulation with low Gd concentration (intra-articular injection; BDPM, 2022). A first equation (Equation (  1 )) is used to calculate the concentration of Gd injected into the French population for each formulation  ([Gd]  i kg Gd per kg body weight).
(2020). This large deviation does not invalidate the model but shows its application limits or its lesser relevance for comparison with a single measurement. The Gd anth flux model integrates wider spatial and temporal scales. The model smooths temporal variability with an annual resolution while the intra-annual variabilities are known (i. e. working days with high pressure on MRIs versus week-end or holidays;  Kümmerer and Helmers, 2000; Lerat-Hardy et al. , 2019). The model also smooths hot spots of Gd anth fluxes (e. g. downstream highly populated area or downstream of wastewater treatment plant plumes;  Bau et al. , 2006). Consequently, the Gd anth flux model based on GBCA consumption data is robust both at the annual scale and at the scale of large watersheds.
e. overestimation; see section 2. 3. 1) and those of the flux model based on GBCA consumption data in France (i. e. spatial and temporal limits; see section 2. 2). Despite the differences observed between the field approach and the modeled results, the model reproduces the orders of magnitude. The European flux model is intended to be more qualitative than quantitative, and its main objectives is to identify the major (current and future) contributors to the European Gd anth flux.
Consumption and fluxes of gadolinium-based contrast agents in France For France, GBCA consumption data between 2014 and 2020 were combined with anthropometric and demographic data to calculate the masses of Gd administered to individuals who had undergone MRI scans with the injection of GBCAs. These masses determine the flux of Gd anth exported to the ocean based on the following postulates: i) 100 % of Gd injected into patients is excreted in the urine  (Joffe et al. , 1998; Kanal, 2016) ; ii) GBCAs are not removed by wastewater treatment plants  (Knappe et al. , 2005; Telgmann et al. , 2012; Verplanck et al. 2010) ; and iii) GBCAs demonstrate conservative behavior in estuaries  (Kulaksız and Bau, 2007). However, several studies have reported transfers of Gd anth from surface water to groundwater  (Brünjes et al.
European fluxes of anthropogenic gadolinium The European Gd anth flux model developed in this study integrated contributions from 48 countries connected to the seaboards of the Atlantic Ocean (including a part of the Arctic Ocean), the Mediterranean Sea, the Baltic Sea and the Black Sea (Fig. 2 ). To the best of our knowledge, GBCA consumption data is not available for all of these countries. However, demographic data from 2015 is available for all countries and the number of annual MRI scans is available for 31 countries (Supplementary Table  A. 2). Our European model is therefore based on results obtained for France (23. 4 mg Gd. inhab -. yr -  and 216 mg Gd per MRI scan) and extrapolated to European countries in 2015. The fluxes obtained based on the number of MRI scans seem more reliable and took precedence over the fluxes obtained based on demographics.
We call upon these communities to refine our model by applying it to local study areas or by integrating new health data into it, such as GBCA consumption data. On a global scale, the major current contributors to the global flux of Gd anth into the ocean are likely countries like the USA  (Hatje et al. , 2016) , Japan  (Inoue et al. , 2020) , and South Korea  (Kim et al. , 2020; Song et al. , 2017). Moreover, many densely populated countries are currently expanding their access to healthcare, and some, such as Brazil, are already recording significant fluxes of Gd anth into the ocean  (Andrade et al. , 2020; de Campos and Enzweiler, 2016; da Costa et al. , 2021; Pedreira et al. , 2018). The creation of observation networks for the main watersheds contributing to fluxes would therefore improve the calibration of the model and help to identify long-term pathways of Gd anth inputs to the ocean.
Acknowledgements  A. J. Santos  would like to thank the  University of Cádiz  and the  Spanish Ministerio de Universidades  for the concession of a "Margarita Salas" postdoctoral fellowship funded by the  European Union -NextGenerationEU  (sol -202100211960- tra). J. Outón  acknowledges the support by the  Spanish Ministerio de Educación y Cultura  through grant  FPU19-02638. University of Cádiz and IMEYMAT are also acknowledged by financing the mutual facilities available at the  UCA R&D Central Services (SC-ICYT) , the  UCA  project references " PUENTE PR2020-003 " and " OTRI AT2019/032 ", and the  IMEYMAT  project reference " LÍNEAS PRIORITARIAS   PLP2021120-1 ". This work was supported by the  Spanish State R&D project (Retos y Generación de Conocimiento)  ref. PID2020-114418RBI00. The regional government of Andalusia with  FEDER  co-funding also participates through the projects  AT-5983 Trewa 1157178  and  FEDER-UCA18-10788.