dataesr/scientific-paragraphs-categorization huggingface dataset:
A C T Users of newly marketed drugs often differ from the patients included in randomized clinical trials, and from patients prescribed similar drugs. Cohorts of such users may be compared using propensity score adjustment, or similar user cohorts may be built using high-dimensional propensity score matching in large population databases. One such database is SNDS, the French nationwide claims and hospitalization database, which covers 99 % of the French population. It has yet been rarely used. To study the comparative effectiveness and safety in secondary coronary prevention of ticagrelor, compared to clopidogrel or prasugrel, we identified in SNDS patients who were dispensed any of the three antiplatelet agents of interest (AE aspirin) within a month after discharge from hospital for acute coronary syndrome (ACS) and followed them one year for recurrence of ACS, stroke, acute bleeding, or death.
High-dimensional propensity scores were developed to identify matched cohorts. Drug performances were also compared in the whole population using adjustment on the same parameters. Here we describe the database that was used, and the methods developed for the high-dimensional propensity score matching, resulting in standardized mean differences between the matched populations of less than 2 % for all of the 500+ variables included in the model. This study was done in a newly available large-scale claims database, which may differ from other population databases, by it size and exhaustiveness The methods elaborate on standard high-dimensional propensity scores as adapted to this claims database.
Method details We describe here the methods used to compare the effectiveness of antiplatelet agents in secondary prevention after an acute coronary syndrome  [2]. The originality of this study is related to -the use of the French National single-payer healthcare database SNDS, which has only recently been made available for researchers. This allows for large population sizes and representativeness (SNDS covers 99 % of the French population from birth to death)  [3]. -the use of high-dimensional propensity score matching, made possible by the large number of subjects and variables in the database, resulting in near-identical comparative cohorts. This study builds on previously published methods, that were developed in the different context of mostly North American claims databases  [1, [4] [5] [6].
Study design The design is a historical cohort study in a national healthcare claims and hospitalisations database, SNDS, including patients hospitalised in 2013 for an ACS with one-year previous history and at least one year follow-up in the database. Data are extracted from 1 st January 2012 to 31 st December 2014: the index date is the date of discharge of the first hospitalisation for an ACS between 1 st January 2013 and 31 st December 2013; -the study follow-up period starts on the study index date and ends one year later. -Each patient has a one-year history before the index date and at least one year of follow-up.
Subjects Extraction criteria. All patients hospitalised in 2013 for an ACS, regardless of the type of treatment (medically managed, managed with PCI or CABG) were identified in the SNDS. Data from 1 st January 2012 to 31 st December 2014 were then extracted for these patients. Study populations. Among these hospitalisations, those with the following criteria were selected for the analysis: first hospitalisation in the year 2013; start and end date of index hospitalisation for ACS between 1 st January 2013 and 31 st December 2013; -without history of ACS in the 30 days prior to start date of index hospitalisation; -index hospitalisation in a hospital, teaching/regional hospital or private hospital; -index hospitalisation duration more than zero days; -adult patients (aged18 years or more) with one-year database history before the index ASC hospitalisation; alive at discharge of index hospitalisation; -with at least one year of follow-up in the database after index hospitalisation (deaths excluded); -without stay in a rehabilitation centre in the 30 days after index ACS hospitalisation (no information on drug utilisation in rehabilitation centre.
Outcomes. Study Outcomes are outcomes that occur during the drug exposure period: -all-cause death (cause of death not available in the database); -hospitalisation for ACS defined as a hospital-discharge summary with primary diagnosis ICD-10 codes I20. 0 (for unstable angina) or I21 (for MI); -hospitalisation for ACS in intensive care defined as a hospital-discharge summary with primary diagnosis ICD-10 codes I20. 0 (for unstable angina) or I21 (for MI) and a stay in an intensive care unit for this hospitalisation; -hospitalisation for an ischemic or undefined stroke defined as a hospital-discharge summary with a primary diagnosis ICD-10 code listed in Appendix B; hospitalisation for an PCI or CABG defined as hospitalisation with Classification Commune des Actes Médicaux (CCAM, French medical classification for clinical procedures) codes listed in Appendix D.
Ticagrelor, Prasugrel vs. Ticagrelor), estimated using high-dimensional propensity score (hdPS), taking into account all informations of the database, with multiple data dimensions from patients and disease characteristics (LTD, one-year history of hospitalisation (primary, linked, or associated diagnosis), one-year history of drugs dispensed and one-year history of healthcare reimbursement (medical or para-medical visits and lab tests). Index diagnosis was a real confounder. So, analysis were conducted according to three subtypes of diagnosis: -unstable angina (UA, I20. 0 ICD-10 codes); -ST-segment elevation MI (STEMI, I21. 0, I21. 1, I21. 2, I21. 3, I21. 9 ICD-10 codes); -non-ST-segment elevation MI (NSTEMI, I21. 4 ICD-10 codes).
Bias Selection bias. Since all patients identified with extraction criteria in 2013 were extracted from a national database, there was no study selection bias and very little attrition (emigration). Information bias. SNIIRAM has the advantages of extracting patient records from an existing database, which are not impacted by study conduct: -hospitalisation for ACS: the index hospitalisation for ACS was defined using the primary diagnosis (ICD-10 codes I20. 0 or I21) at discharge in order to exclude ACS coding for suspicion or exploration, as well as miscoding for another disease. A sensitive definition was defined including all cardiac revascularisations and a specific definition including only ACS with intensive care stay during hospitalisation; -APA exposure: APA exposure was assessed using the first dispensing after discharge and exhaustive non-hospital drug claims.
Consequently, this analysis did not take into account APA switch before discharge, but the proportion of patients concerned should be low and the impact over one year would have been mainly in relation with the APA prescribed for a long time; -outcomes: since deaths are recorded in the database using the national death registry, there is no information bias for this outcome. Clinical events were defined using the primary diagnosis (ICD-10 code) at discharge. As for the index hospitalisation for ACS, miscoding cannot be excluded but had to be sparse for the clearly defined events studied. Nevertheless, the PMSI coding is fully independent from the study and there is no reason that a potential miscoding is different between APA drugs, excluding an information bias; Confounding bias. The choice of APA can be in relation with patient, his history or the characteristics of the index hospitalisation for ACS.
Data source The study was done in SNDS, the French National healthcare system database. This database includes 99 % of the French population, from birth or immigration to death or emigration  [3]. The SNDS (SNIIRAM) database is the national healthcare insurance system database linked to the national hospital-discharge summary database (Programme de Médicalisation des Systèmes d'Information, PMSI) and the national death registry. It includes the main healthcare insurance systems (CNAMTS including civil servants and students, MSA, and RSI plus secondary minr systems,), which represent 99 % of the French population. Non-hospital healthcare data are available since 2003 for CNAMTS and 2011 for MSA and RSI. Hospital discharge information is available since 2005 for CNAMTS and 2010 for MSA and RSI populations.
Non-hospital data are updated every month and hospitaldischarge summaries yearly at end of Q3 for the previous year. The SNIIRAM contains individual anonymous information on: -General characteristics: gender, year of birth, Couverture Mutuelle Universelle-complémentaire (CMU-c) status, residence area; -Date of death for those concerned; Causes of death are not available at this time but will be in the near future. -Long-term disease with full insurance coverage (LTD and their associated ICD-10 codes) with start and end date of LTD. There are 30 LTD, which include most chronic diseases with long term and/or expensive treatment, e. g. LTD 13 is for coronary heart disease. Registration with an LTD is obtained at the request of a patient's general practitioner and must be validated by the health insurance system physician.
-Lab tests and other procedures are recorded, but not their results. In some cases the results may be presumed from actions taken after the lab tests or procedures e. g. , a test for H. pylori followed by HP eradication therapy will probably indicate that the test was positive; Liver function tests or enzymes followed by hospital admission for acute hepatotoxicity will probably have been abnormal; A home intervention by a nurse shortly after the dispensing of an injectable drug will probably indicate its injection by the nurse. Of course all these are conjectural but rely on medical knowledge and common sense. These data also include cost, so that medical costs from the insurer's viewpoint can be derived relatively easily. Overall yearly cost per patient may also be used as an indicator of general health.
The access to the SNIIRAM is regulated and needs approval from the "Institut national des Données de Santé" (INDS, National Institute of Healthcare data) and the "Commission Nationale Informatique et Libertés" (CNIL, the French data protection commission). Database extraction criteria must be fully described in a Data Extraction Plan (DEP) approved prior to initiating extraction. Data are extracted by SNIIRAM data manager and made available on a dedicated portal, or transmitted on encrypted hard drives.
Data extracted from SNDS To define the study population, the hospital records indicating ACS (primary diagnosis ICD-10 codes I20. 0 and I21) were extracted from 1 January 2013 to 31 December 2013; For the study population, the following data were extracted for the years 2012, 2013 and 2014: demographic data: gender, year of birth, month and year of death (for those concerned), CMU-c, residence area; -LTD status with LTD 30 code, associated ICD-10 code, starting and ending date; -hospitalisations with admission and discharge dates, duration of stay, ICD-10 codes diagnosis (primary, related and associated), GHM codes, in-hospital procedures (CCAM codes, TNB codes, LPP codes), department of stay, destination after discharge and external consultations; treatment reimbursement with date of dispensing, long name, CIP codes, ATC codes, number of packs/units dispensed and dosage, as well as speciality of the prescriber and date of prescription; -medical visits with date, type, physician specialty, type of activity (private / public); -non-hospital medical procedures with date, number and type of procedure (CCAM code);-lab tests with date, number and type of test (TNB code); -paramedical consultations and acts (nurse, physiotherapist, orthophonist.
Populations and variables analysed A flow chart depicting the total number of patients in the database satisfying the extraction criteria and the number of patients for the study population will be prepared. The following analyses were performed for the whole population and according to the different APA prescribed after discharge (including no APA treatment): -description of the characteristics of patients hospitalised for ACS: initial APA treatment; gender, age at inclusion, CMU-c; index ACS hospitalisation using main diagnosis, procedure performed during hospitalisation, hospitalisation duration; -description of the one-year period prior to index date: history of cardiac diseases using LTD, diagnosis of previous hospitalisations, related and associated diagnoses to index ACS hospitalisation; history of dispensings of drugs; cardiac risk factors using LTD, diagnosis of previous hospitalisations, related and associated diagnoses to index ACS hospitalisation and one-year specific drug reimbursement history; other comorbidities using LTD, related and associated diagnoses to index ACS hospitalisation, oneyear history of hospitalisations and one-year other drug reimbursement history; description of the one-year period after index date: hospitalisations with details of diagnoses; medical visits, with details of physician speciality; lab tests, with details of type of test; dispensings of all drugs; usage patterns of each APA: frequency, quantities, MPR, co-prescription, duration and persistence (discontinuation, switches, retention rates).
Descriptive analysis The descriptive analysis of qualitative and ordinal variables were presented using the frequency and the proportion of each modality, including missing data as modality. In stratified analyses, the percentages and distributions were estimated in columns in each stratum. Quantitative variables were presented using the size, number of patients with missing data, the arithmetic mean, standard deviation, the first quartile, median, third quartile, and extreme values. Graph (flowchart, histogram) were used to present the selection of patients. The SNIIRAM database covers actually more than 95 % of the French population. As the study population is not a sample, two-sided tests and 95 % confidence intervals (CIs) was not performed. Standardized differences were presented to assess size effects between treatment groups in crude, adjusted and matched analysis.
High dimensional propensity score The probability to be treated by individual APA (Ticagrelor vs. Clopidogrel and Ticagrelor vs. Prasugrel) after the index ACS hospitalisation (according to index diagnosis) was estimated using a hdPS. The hdPS algorithm  (Rassen et al. , 2011)  grounds on automated technique (available in a SAS 1 macro) that examines thousands of covariates among different claims data dimensions in the study population. d ¼ c P 1 À c P 2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi c P 1 1 À c P 1 þ c P 2 1 À c P 2 h i 2 s d ¼ X 1 À X 2 À Á ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi S 2 1 þ S 2 2 2 s Each dimension describes an aspect of care and common data dimensions include patient characteristics (age, gender, CMU-c, index PCI, index CABG, duration of the index hospitalisation, at least one dispensing of ASA before index date, category of hospital of the index hospitalisation, intensive care during index hospitalisation, incident ACS/naïve APA, Charlson comorbidity index), history of disease (LTD), hospitalisation (diagnoses), healthcare reimbursement (medical or paramedical visits and lab tests) and drug dispensing (See Appendix F).
In conclusion In this large population database that is newly accessible, the use of high-dimensional propensity score matching on large numbers of routinely collected variables results in essentially identical populations on over 500 parameters, including direct or indirect potential confounders. 1) Chronic diseases (ICD-10 code 3 digits) present before index date, 2) Hospitalization diagnoses (ICD-10 code 3 digits) in the year before index date, 3) Cardiovascular drugs dispensings (ATC code 7 digits) in the year before index date, 4) non-cardiovascular drugs dispensings (ATC code 7 digits) in the year before index date 5) medical or paramedical visits or lab tests (NABM code) in the year before index date These binary covariates are then individually assessed for selection into a propensity score using a logistic regression model.
Therefore, climate can affect the shape of a SAD, and must be accounted for when evaluating the relative importance of other factors such as disturbances. Here we evaluate the relative importance of island geography (area and isolation) and cyclone disturbance regime on SADs in local, plot-scale, communities. We analyse how a measure that combines cyclone frequency and intensity affect the shape of tree SADs in humid tropical forest using a large dataset covering 20 islands located in the Indo-Pacific region. We hypothesized that the size and isolation of islands would affect SADs through their effect on the island species pool  (Ibanez, Keppel, et al. , 2018). Due to lower species richness, communities located on small and isolated islands should exhibit lower proportions of locally rare species and more lognormal-like SADs than communities located on larger and less isolated islands.
Accepted Ar ticle 'This article is protected by copyright. All rights reserved. ' Neumann, 2010). The IBTrACs database provides information on the position, maximum sustained wind speed and translation speed of each inventoried cyclone at 6-h intervals. We used this data to calculate the average frequency of tropical cyclones for each grid cell with a spatial resolution of 1° latitude × 1° longitude (≈ 12345 km 2 at the equator). Only events reaching maximum sustained wind speed > 17 m. s -  at some point along the cyclone track were considered, i. e. tropical storms and category one to five tropical cyclones according to the Saffir-Simpson Hurricane Scale. Explanatory variables were not strongly correlated (Spearman's Rho < 0. 70, see Table  S1 ).
Accepted Ar ticle 'This article is protected by copyright. All rights reserved. ' The α parameter was estimated for each plot using the gambin R package  (Matthews et al. , 2014). Although the mathematical formula for estimating α does not have parameters for the number of individuals and species (see  Ugland et al. , 2007) , the possible shapes of SADs are highly dependent on the number of individuals and species  (Locey & White, 2013; Matthews et al. , 2014). Given that the number of trees per plot varied about 3-fold in our data set, α was also estimated controlling for different sampling sizes (α stand ). Standardization was done by randomly subsampling 400 trees per plot before fitting gambin models, and repeating the process 100 times for each plot before estimating α stand as the mean value of α i from all iterations.
Analyses We first used generalized linear models to analyse (i) how variation in α and α stand were associated with the number of trees and the number of species, (ii) how variation in α, α stand , the number of trees, and the number of species were associated with island geography (log-transformed Area and Dist) and cyclone disturbance regime (PDI), and (iii) the direct and indirect effects (through their effects on the number of trees and species) of Area, Dist, PDI, on α and α stand. For α and α stand (skewed continuous variables) we used a Gamma distribution while for the number of trees and the number of species (discrete variables) we used a Poisson distribution. In both cases a log linkfunction was used. Secondly, we used the MuMIn R package and the dredge function to generate and compare different sets of models representing all possible combinations and subsets of fixed effects (PDI, Area, Dist), and their two-way interaction terms in explaining variation in α, α stand , the number of trees, and the number of species.
Accepted Ar ticle 'This article is protected by copyright. All rights reserved. ' Figure  2  Examples of SADs with increasing α parameters from our dataset. The abundance of species on the x axis were binned into octaves using log2 transformation (i. e. octave 0 represents species with 1 individual, octave 1 species with 2-3 individuals, octave 2 species with 4-7 individuals and so on). The y axis represents the relative number of species in each octave (relatively to the total number of species in the plot). Blue bars represent observed SADs and white boxes represent SADs as predicted by the alpha-gambin model. The left plot represents SAD in a 1-ha plot located in Brunei  (Borneo, Small et al. , 2004)  with 417 trees of 187 species, the middle plot represents SAD in a 1-ha plot located on Grande Terre (New Caledonia,  Ibanez, Blanchard et al.
Acknowledgements T. Ibanez was funded by the  Agency for Economic and Environmental Development (DDEE)  of the North Province of New Caledonia as well as by the  National Park Service, Pacific Island Inventory & Monitoring Network  through a cooperative agreement # P17AC00835  and. G. Keppel was partially supported by an  Alexander von Humboldt  fellowship. Funding by the  Hermon Slade Foundation  ( HSF 19105 ) facilitated some of the analyses and writing. We thank all people who contributed to collecting plot-data. We thank  Tom Matthews  and one anonymous reviewer for helpful comments on the manuscript.
5 BCP have improved but large uncertainties remain (from 5 to 20 GtC yr -1 exported below the euphotic zone or mixed layer depth). The 234 Th technique is widely used to measure the downward export of particulate organic carbon (POC). This technique has the advantage to allow a downward flux to be determined by integrating the deficit of 234 Th in the upper water column and coupling it the POC/ 234 Th ratio in sinking particles. However, the factors controlling the regional, temporal and depth variations of POC/ 234 Th ratios are poorly understood. We 10 present a database of 9110 measurements of the POC/ 234 Th ratio in the ocean, from the surface down to >5500 m, sampled on three size fractions (~>0. 7 µm, ~1-50 µm, ~>50 µm), collected with in situ pumps and bottles, and also from bulk particles collected with sediment traps.
15 papers and open datasets available online. Unpublished data has also been included. Multiple measurements can be found in most of the open ocean provinces. However, there is an uneven distribution of the data, with some areas highly sampled (e. g, China Sea, Bermuda Atlantic Time Series station) compared to some others that are not well represented, such as the southeastern Atlantic, the south Pacific and the south Indian oceans. Some coastal areas, although in a much smaller number, are also included in this global compilation. Globally, based on 20 different depths horizons and climate zones, the median POC/ 234 Th ratios have a wide range, from 0. 6 to 18 µmol dpm -1.
145 https://doi. pangaea. de/10. 1594/PANGAEA. 911424 (Puigcorbé, 2019). Latitude, longitude and sampling dates are reported. When dates of the individual stations were not reported in the original publications, we allocated the midpoint of the sampling period as the sampling date. The same was done when the specific sampling coordinates were not available. The database consists of 9110 measurements of POC/Th ratios in the ocean. Particles were collected using in situ pumps (ISP), water collection bottles (CB) and sediment traps (ST). We refer to "bulk".
150 (BU) for particles sampled using CB and ISP with a pore size filter of 0. 2-1 µm. For this group of samples, particles >0. 7 µm were collected using GFF filters and >1 µm using QMA filters. In some particular cases other types of filters, with a different pore size (e. g. , 0. 2 µm, 0. 45 µm or 0. 6 µm) might have been used (see database for details). Hereafter, we use >1 µm for the bulk particles. We refer to "small particles" (SP) for particles usually collected using ISP on a 1-50 µm mesh size and "large particles" (LP) for particles usually collected using ISP on 155 mesh size >50 µm (see details on other size ranges also used in the database). Finally, some POC/Th ratios were measured in sinking particles sampled using sediment traps (ST). Figure  1  shows the global distribution of POC/Th ratios grouped by these four categories: BU, LP, SP and ST.