dataesr/scientific-paragraphs-categorization huggingface dataset:
Lastly, few existing multicenter studies consider appropriate control groups and none of the current studies exploit disease trajectories of progression in specific time windows, nor in calendar time  [16] [17] [18] [19]. In this study, we extracted, consolidated, harmonized, and analyzed EHR data from an international cohort of patients from the healthcare systems participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). The 4CE Consortium is a research collaborative across seven countries that uses EHR data in a federated manner to study the epidemiology and clinical course of COVID-19  20, 21. The 4CE network of researchers manually ran database queries returning only aggregate counts and statistics on data representative of 414,602 patients infected with SARS-CoV-2 and 2.
Baseline prevalence and acute period incidence of conditions Our dataset encompassed over 920 medical conditions as defined by phenotype code (PheCode) from the Phenome-wide association studies (PheWAS) catalog of phenotypes  22, 23. When compared to inpatient controls, inpatient COVID-19 cases had a higher baseline prevalence of type 2 diabetes, gastroesophageal disease, obesity, chronic kidney disease, respiratory abnormalities, and heart failure (Fig. 2a ). Among inpatient COVID-19 cases, conditions with the highest cumulative incidence during the acute stage included viral pneumonia, acute kidney injury, respiratory abnormalities, primary hypertension, malaise, and fatigue (Fig. 2b ). When compared to inpatient controls, inpatient COVID-19 cases had a higher cumulative incidence of viral pneumonia, respiratory abnormalities, pneumonia, malaise, fatigue, acute kidney injury, and hypovolemia.
This study used a federated approach, in which standardized and straightforward database queries were distributed to sites to run locally on their EHR data, and only aggregate counts and statistics were shared externally. This approach lowered regulatory barriers, streamlined the institutional review board (IRB) approval process at sites, and enabled sites to contribute to the analyses with minimal resources. Using this approach, we obtained a broad data-driven view of PASC across different countries, healthcare systems, patient populations, and time periods, and systematically examined all medical conditions across the different comparison groups. Central to our consortium effort is the ability of each local site to perform quality control by its own data scientists and clinicians. Other consortia, including Observational Health Data Sciences and Informatics (OHDSI) and Patient-Centered Clinical Research Network (PCORNet), have had similar success with federated EHR data networks  24, 25.
Fourth, the study likely has several time-dependent biases: (1) not all patients had the same follow-up time in the study period, particularly in the late-stage period (90+ days after the index date); (2) we could not account for competing risks such as from death; (3) diagnosis codes may have been subject to censoring (transfer, discharge, death, and other loss to follow up) and thus dropout bias. Fifth, EHR data can have quality and completeness problems, especially for recent data, due to coding lag and pre-final codes. The degree to which this might have biased our analyses is likely the greatest in the final 2021-Q1 time period and depends on when individual hospitals ran their local database queries. Considering the aforementioned limitations, we caution against strong inferences from this study, which can identify associations and not identify mechanisms nor assess causality.
Cohort identification All patients who had a SARS-CoV-2 reverse transcription PCR test result recorded within the healthcare system were included in the data collection. COVID-19 patients were further classified as hospitalized (inpatient) or non-hospitalized (outpatient) based on whether or not they had a hospital admission between 7 days before or 14 days after a positive PCR test. If a patient had multiple positive PCR tests, the first positive PCR test was used. Inpatient COVID-19 cases' index date was defined as the hospital admission date, and outpatient COVID-19 cases' index date was defined as the date of the first positive PCR test. Patients with one or more negative PCR tests, no positive PCR tests, and no U07. 1 ("COVID-19, virus identified") ICD-10 diagnosis codes were defined as controls.
Federated data collection Our analyses were performed on EHR data collected from 277 hospitals (affiliated with 17 regional healthcare systems) across five countries: France, Germany, Italy, Singapore, and the United States  20, 65. In the United States, we grouped the 170 Veterans Affairs hospitals into five regional healthcare systems  66. See Table  2  for details of participating healthcare systems. The data cover information from January 1, 2020 to March 30, 2021; patient cohorts were additionally stratified by the calendar quarter of their index date to account for temporal changes in incidence, treatment, and SARS-CoV-2 variants, which of course were heterogeneous among the countries. We distributed a SQL database script to contributing healthcare systems, which was manually run locally on EHR data to generate aggregate counts and statistics on patient cohorts after gaining local IRB approval  20, 65, 67.
The script was designed to run on clinical data repositories based on the Informatics for Integrating Biology & the Bedside (i2b2) data model, though several sites ported the code to their own data models if they did not use i2b2. Versions of the SQL script for both Microsoft SQL Server and Oracle databases are freely available on GitHub with an Apache 2. 0 open source license  68. Healthcare systems manually uploaded their aggregate result files to a central 4CE data upload website. Data collected included counts of patients, demographic characteristics, and truncated International Classification of Diseases (ICD) codes, Ninth or Tenth Revision, at three digits. In order to ensure high-quality EHR data across countries, healthcare systems, and cohorts, multiple data quality control steps were performed.
The 4CE data upload website ran an initial online quality control step, which checked that all files were under the standard format. This included the verification of the file and column names, column orders, data types, code values and ranges, and ensuring that there are no duplicated records. At the central site, additional quality control steps were completed on all submitted data. These steps included cross-validation consistency of the total case counts across all cohorts and verification of no negative values in patient counts. The central site also checked for consistency between the 3-digit ICD codes and the ICD dictionary. If a healthcare system presented any quality control issues, the central site directly contacted its corresponding informaticians to resolve them. These steps were crucial in ensuring proper downstream statistical analysis.
ACKNOWLEDGEMENTS Z. X. is supported by the  NIH National Institute of Neurological Disorders and Stroke (NINDS)   R01NS098023. B. W. Q. T. is supported by  National Medical Research Council   Research Training Fellowship  ( MOH-000195-00 ). M. M. is supported by  NIH National Center for Advancing Translational Sciences (NCATS)   UL1TR001857. S. V. is supported by  NCATS   UL1TR001857. L. P. P. is supported by  NCATS CTSA  Award # UL1TR002366. D. A. H. is supported by  NCATS   UL1TR002240. S. E. M. is supported by  NCATS   UL1TR002240. S. N. M. is supported by  NCATS   5UL1TR001857-05  and  NIH National Human Genome Research Institute (NHGRI)   5R01HG009174-04. G. S. O. is supported by  NIH  grants  P30ES017885  and  U24CA210967. F. J. S. V. is supported by  NCATS  Grant # UL1TR001881. R. K.
is supported by  NCATS   UL1TR001998. Y. L. is supported by the  NIH National Library of Medicine (NLM)   R01LM013337. R. B. is supported by  EU  PROJECT  H2020 PERISCOPE-101016233. K. C. is supported by VA  MVP000  and  CIPHER. N. G. , Z. S. H. A. , and S. L. are supported by  NLM   T15 LM007092. B. J. A. is supported by  NIH National Heart, Lung, and Blood Institute (NHLBI)   U24 HL148865. K. B. W. is supported by  NHLBI   R01 HL151643-01. A. M. S. is supported by  NHLBI   K23HL148394  and  L40HL148910 , and  NCATS UL1TR001420. G. M. W. is supported by  NCATS   UL1TR002541  and  UL1TR000005 ,  NLM R01LM013345 , and  NHGRI   3U01HG008685-05S2.
1. The reported costs of invasive alien species from the global database InvaCost are heterogeneous and cover different spatio-temporal scales. A standard procedure for aggregating invasive species cost estimates is necessary to ensure the repeatability and comparativeness of studies. 2. We introduce here the invacost r package, an open-source software designed to query and analyse the InvaCost database. We illustrate this package and its framework with cost data associated with invasive alien invertebrates. 3. First, the invacost package provides updates of this dynamic database directly in the analytical environment R. Second, it helps understand the heterogeneous nature of monetary cost data for invasive species, processes to harmonize the data and the inherent biases associated with such data.
A promising solution lies in opensource software providing frameworks to openly share data and methods altogether (e. g. Michener & Jones, 2012). Therefore, we developed the invacost r package as a tool to query and investigate InvaCost economic costs of IAS world-wide  (Diagne et al. , 2020a). This database is global in extent and covers many taxonomic groups, ecosystem types, activity sectors and temporal and spatial scales. The invacost r package and its framework have already been used, thus far, in 29 publications to describe the economic costs of biological invasions at multiple spatial scales (e. g. Diagne et al. , 2021; Haubrock et al. , 2021). We developed the invacost r package with three objectives. The first was to provide the up-to-date database directly into R, relieving users from the burdens of compatibility issues and errors associated with loading such a large dataset in R.
Second, the package helps users understand the nature of monetary cost data for IAS and the inherent biases associated with such data with a step-bystep tutorial provided with the package. The third objective was to provide two complementary ways to analyse these data. One is a standard method to derive cumulative and average cost values over different periods of time, with relevant visualization methods. The other derives the trend of costs over time with different modelling techniques accounting for the statistical issues of such econometric datasets, such as nonlinearities, heteroskedasticity, temporal autocorrelation and outliers. By meeting these objectives this software provides widespread access to these data and facilitates comparisons across studies in a straightforward manner. However, we strongly recommend working with the invacost data in interdisciplinary teams that incorporate social science expertise (i.
e. economics) in order to match each specific problems with the most suitable methodological choices in handling and avoiding improper use of the data. For maximum flexibility for addressing individual researcher needs, we encourage users where necessary to duplicate the package source code and adapt it to their needs, for example altering the standardization across currencies. This possibility to adjust the code as best suited to one's needs, coupled with the necessary economic expertise allows for flexibility and versatility in answering different questions using the necessary tools and suitable conditions. In the following sections, we describe the rationale and methods implemented for these objectives along with relevant literature. We do so with the illustration of a simple case study on the global monetary costs caused by invasive invertebrates (i.
| THE i n va c o s t r PACK AG E The package requires a standard installation of R (version≥4. 0. 0) and is available on the Comprehensive R Archive Network (see Appendix A. 1 for a code example). Upon installation eight dependencies will be automatically installed: dplyr  (Wickham et al. , 2020) , earth (Milborrow, Derived from mda:mars by Hastie T and Tibshirani R, & Uses Alan Miller's Fortran utilities with Thomas Lumley's leaps wrapper, 2019), ggplot2  (Wickham, 2016) , lmtest  (Zeileis & Hothorn, 2002) , mgcv  (Wood et al. , 2016) , quantreg  (Koenker, 2020) , robustbase  (Maechler et al. , 2022) , sandwich  (Zeileis, 2004)  and scales  (Wickham & Seidel, 2020). All the package code is opensource, available on the GitHub repository, where users can also contribute or submit issues: https://github.
| OBJEC TIVE 1-QUERYING , CLE ANING AND FILTERING THE Inva Cos t DATABA S E The InvaCost database is a dynamic database where existing information can be corrected and new data regularly added. Every new release of the database is checked for errors and inconsistencies with dedicated testing procedures in the package. The latest version of the InvaCost database (accessible at https://doi. org/10. 6084/m9. figsh are. 12668570) is shipped with every release of the R package. The database can be accessed with the command data(invacost). To make sure that users understand the database and the package, we provide a step-by-step tutorial with thorough explanations on the GitHub repository (https://github. com/Farew e/invacost). The database loaded in R contains over 60 fields (see here for a full description https://doi.
org/10. 6084/m9. figsh are. 12668570 and here for frequently asked questions https://farewe. github. io/ invac ost_FAQ /, such as (a) how to collate data from the literature F I G U R E 1 Conceptual framework of analysing monetary costs of biological invasions within the invacost r package. We illustrate this framework with the example of a subset of the database (invertebrates, i. e. , all non-chordate animals). The framework depicts the three objectives detailed in the manuscript. We indicated in green the functions used in the invacost package. In 1 and 2, we illustrate with simplified tables how the structure of the database changes as the database is subsetted, filtered and then expanded. The cost over time graphs in 3a and 3b illustrate the native graphical outputs implemented in the package.
| OBJEC TIVE 2-HOMOG ENIZ ATION OF COS TS: E XPRE SS I ON IN ANN UAL COS TS AND E XPAN S I ON TO THEIR RELE VANT TIME PERIODS Once the relevant filters have been applied to the database, extracting meaningful cost estimations from the resulting subset of cost records requires accounting for the fact that cost entries in the database have different temporal coverages: entries can be one-time costs, annual costs with repetitions over multiple years or total costs of impacts which spread over multiple years. Therefore, to be comparable, cost estimates must be homogenized with a two-step process. First, they must be expressed with the same temporal unit, where the most relevant choice is annual costs. This step is already accounted for in the database with fields containing 'cost_estimate_ per_year' in their names.
Second, once they have been homogenized on an annual basis, costs must be applied to their relevant time periods, that is, repeated for each year over which the monetary impact was reported. This step is performed with the expandYearlyCosts The expansion step requires adequate information with respect to the beginning and ending years of cost impacts. However, information on the beginning and ending years was not directly provided in the literature sources of monetary costs for 23% of entries in the database (2,166 rows of data). Therefore, for each source for which it was not available, educated guesses were made on the probable starting and ending years, and included these guesses in the columns 'Probable_starting_year_adjusted' and 'Probable_ending_year_adjusted' columns  (Diagne et al.
, 2020b). Because these columns are based on conservative assumptions (e. g. the ending year of costs does not extend beyond the publication year), they should limit over-estimation; hence, it is recommended using these columns (see discussion and Extended Data figure  6  in  Diagne et al. , 2021). Consequently, this process requires removing any cost entry for which the period impact could not be extracted from the source material. Once the homogenization step has been performed on all cost entries in the user's consolidated subset of the database, extractions and analyses can be performed to explore the patterns of costs of IAS. In our example, after expansion the data on invertebrate costs contained 4343 rows (Figure  1. 2, code in Appendix A. 3), each representing a single year of costs for a specific species/group.
| OBJEC TIVE 3A-E S TIMATING THE CUMUL ATIVE AND AVER AG E REP ORTED COS TS OF INVA S I ON S The first method to analyse monetary costs of IAS consists of calculating the cumulative and average costs over time using cost estimates, as they appear in the filtered and homogenized material. These costs can be investigated on an annual basis, over the entire period covered by the database, or over a series of time intervals to account for the evolution of costs over time. All these options are performed simultaneously with the function summarizeCosts. First, this function calculates the sum of costs for each year of the time period requested by the user. That is by default, from 1960 (the first year with available conversion factors necessary to standardize the costs into 2017 USD as previously mentioned using exchange rates or PPP and inflation rates available through the WorldBank website for example) to the last year of the dataset.
Second, it computes the cumulative total costs and average annual costs over the requested period. Last, it computes cumulative and average annual costs for user-defined time intervals (by default, 10 years) in the requested period. A typical usage of this function is to automatically derive cumulative or average costs for specific subsets of the database, such as for specific geographical areas, type of cost or on a per-species basis (see the detailed example on how to derive per-species cumulative cost estimates in the online tutorial https://github. com/ Farew e/invac ost#examp le-on-many-subse ts-all-taxas pecie s-inthe-database). In our example on the costs of invasive invertebrates, the function yielded a cumulative cost of 2017 USD 462. 3 billion for the 1970-2020 time period, which corresponded to an annual cost of 2017 USD 9.
| CON CLUS ION In conclusion, we hope that the invacost r package will be beneficial to researchers and stakeholders, by providing the most up-to-date version of the global database on economic impacts of IAS directly in R-with a series of standard and robust methods to extract, analyse and compare cost data. This is the first R package to address global costs of IAS, but it follows the philosophy of similar software facilitating access to large datasets across disciplines (e. g. letsR,  Vilela & Villalobos, 2015). A next step to promote access to a larger audience, beyond regular R users, will be to make all functions and the content of the package (including the database itself) accessible in a user-friendly interface, for example, through interactive tools like Shiny apps (https://shiny.
rstud io. com/). The access facilitated by the invacost package has already fostered new research opportunities on the impacts of IAS. For example, the package has been used in all 19 articles of the NeoBiota special issue on the geographic distribution of monetary costs of biological invasions around the world, which ultimately aim to inform decision makers at relevant scales  (Zenni et al. , 2021). Hopefully, the invacost package will therefore be used as a powerful tool to complement assessments of the diversity of impacts of biological invasions, and better inform decision-makers from sub-national scales (e. g. Manfrini et al. , 2021)  to international assessments (e. g. the Intergovernmental Science-Policy Platform on Biodiversity & Ecosystem Services).
Significant increase in costs over Ɵme 9 Convergence across models 9 Predicted annual cost for 2014: US$ billion 26. 4 -62. 0 9 Non-linear trends pinpoinƟng dataset specificiƟes 9 CumulaƟve cost: US$ billion 462. 3 in total 9 Average annual cost: US$ billion 9. 1 / year 9 Increase in decadal averages.
function. This function relies on the fields indicating the starting and ending years of the annual costs. For example, reference ID 1619 reports a cumulative eradication budget of € 550,000 for Anoplophora glabripennis in France between 2003 and 2008. A preliminary step, already included in the InvaCost database standardizes the costs into a common currency. That is, conversion from local currency to US Dollars (USD) using the exchange rate or, for a better consideration of the difference of price levels among countries, the Purchasing Power Parity (PPP) and then inflation into 2017 USD (see Diagne, Catford, et al. , 2020  or the online tutorial of the invacost r package for details). For the purposes of the standardization Diagne, Catford, et al. (2020)  chose to first convert the costs from the local currency to USD and then use the appropriate inflation rate.
It is important to note that the order of this process matters in determining the ultimate cost and that reversing it (i. e. inflating first in the local currency and then converting in USD) may lead to different numerical results. In the aforementioned case of ID 1619, this yields an annual cost of 2017 USD 136,437 for that period. The expansion step implemented in our package replicates this standard annual cost over each year of the impact period (2003 ( -2008, Figure 1. 2), Figure 1. 2). The costs are not expanded for the database by default because the database is easier to distribute in the compact form and because expanding the costs requires decisions which should be assessed by the user, depending on the research question addressed.
package is an ideal tool to help achieve novel scientific assessments of invasion impacts, not merely the reported costs. Specifically, the continued improvement of the invacost package as well as the interoperable nature of the dynamic InvaCost database will enable: (a) the linkage of cost estimates to established indicators of alien impacts world-wide (e. g. GRIIS: Global Register of Introduced and Invasive Species, Pagad et al. , 2018; SEICAT: Socio-Economic Impact Classification of Alien Species, Bacher et al. , 2018) to ensure a standardized assessment of IAS impacts across regions and socio-economic sectors over time; (b) investigation of the relationships between invasion costs, ecological traits and phylogeny of IAS to better understand and predict impacts; (c) movement towards predictive approaches to support, evaluate and prioritize cost-efficient management strategies according to various scenarios of invasions in our changing global environment (Lenzner et al.
9 Significant increase in costs over Ɵme 9 Convergence across models 9 Predicted annual cost for 2014: US$ billion 26. 4 -62. 0 9 Non-linear trends pinpoinƟng dataset specificiƟes 9 CumulaƟve cost: US$ billion 462. 3 in total 9 Average annual cost: US$ billion 9. 1 / year 9 Increase in decadal averages.