dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection In this study, data were collected on four COVID-19 outbreaks in China from corresponding studies and open accesses, which comprised of transmission caused by the Delta The data collected included basic information about the patients (age, sex, occupation, address, whether vaccinated, and doses), clinical typing (asymptomatic infection, minor, ordinary, heavy, and severe), key time points of transmission (date of exposure, date of onset, date of the positive test, and date of confirmation), and transmission chains between cases. Population information for each site was obtained from the 7th National Census data in 2020.
SI and epidemic curve-based method Through the transmission chains, which are obtained from the collected COVID-19 data, we count the standard deviations and means of serial intervals (SI) of onset dates of outbreaks. Then, we calculate the reproduction number, denoted as R t , by the EpiEstim package in R software. R t represents the average number of second-generation infections from firstgeneration cases per unit time in a susceptible population. Refer to Supplementary material for a detailed calculation. SI = onset time of second -generation cases -onset time of first -generation cases (4).
Statistical analysis Data entry and organization related to this study were performed in Excel 2019. Continuous quantitative variables were described by median ± interquartile range (IQR) and categorical qualitative variables by percentages. Statistical analysis was performed using SPSS version 22. 0, and differences were statistically significant at p ≤ 0. 05. Graphs were plotted using Graph Prism 7. 0; transmission chains were plotted using OrignPro version 2022; models were fitted using Berkeley Madonna 8. 3. 18; differential equations were solved using the fourth-order Runge Kutta method; and model convergence was based on the root least mean square (LRMS), further using the coefficient of determination (R 2 ) to determine the goodness of fit. The R t was calculated using the EpiEstim package in R software.
Discussion Evaluating the transmissibility of COVID-19 is of significance in the prevention and control of the pandemic. In this study, we obtained the reproduction numbers of COVID-19 through the definition method, the next-generation matrixbased method, and the SI and epidemic curve-based method, with outbreak data collected from four different regions of Delta and Omicron variants of SARS-CoV-2. Then, we made a comparison of the reproduction numbers obtained from different methods and looked into the merits and demerits of those calculation methods and their reproduction numbers in actual situations, so as to provide public health departments with a valid and reliable index for evaluating the transmissibility of COVID-19 and a theoretical basis for implementing intervention measures. In selecting the outbreak data, we have chosen the outbreaks in region H in July 2022, in region P and X in September 2021, and in region Z in January 2022.
Limitations Due to technical conditions and data limitations, this study has the following limitations. First, the data used in this study are one provincial Delta variant outbreak, two municipal Delta variant outbreaks, and one municipal Omicron variant outbreak. As a comparative study, data information on the corresponding scale of the two types of variants should be guaranteed. Second, there are several ways to calculate the reproduction number, but in this study, only three methods were used for the calculation of the reproduction number, which can be further calculated by different methods and further compared to draw more reliable conclusions.
Available Data. Hospitalization Data. Hospitalization data are extracted from the SI-VIC database (Système d'Information pour le suivi des VICtimes), a governmental system created in 2016 in order to identify and follow victims in exceptional circumstances (e. g. terror attacks). Since March 18 th 2020, the SI-VIC database provides, to Santé Publique France, the daily number of hospitalized COVID-19 patients, at multiple geographical scales. In this work, we focus on the 12 French non-insular regions. An entry in the SI-VIC database corresponds to a patient hospitalized in connection with COVID-19. Specifically, it requires the presence of at least one of two criteria: i) a biologically confirmed diagnosis of COVID-19 (e. g. RT-PCR positive test result), or ii) a chest CT scan suggestive of COVID-19.
m -3 (AH) -measured by meteorological stations were extracted from the National Oceanic and Atmospheric Administration database using R package worldmet. Regional daily means were estimated with all stations located in the region or at less than 10 km from the region border. To account for variations of population density across a region, a weighting was used based the on population living in a 10km buffer around each station, giving more importance to weather conditions around densely populated areas. We use the PREDICT index of weather transmissibility of COVID-19 (IPTCC), as defined by  Bukhari and Jameel (2020)  and  Roumagnac et al. (2021) : IP T CC = 100 e - 1 2   (T -7. 5) 2 196 + (RH -75) 2 625 + (AH -6) 2 2. 89  . As for interpretation, this rough index ranges from 0% to 100%, the smaller the less favorable are the conditions for COVID-19 transmissions.
This variable is denoted W i (t) for the i th region. When taking W = 0, one considers the global average value over all French regions over the study period. VoC of SARS-CoV-2. Some variants of the SARS-CoV-2 virus have been classified by national and international health authorities as VoCs as they impact transmissibility or virulence, or decrease the effectiveness of measures World Health Organization. Since January 2021, French health authorities have conducted surveys to estimate the prevalence of three VoCs: 20I/501Y. V1 (Alpha), 20H/501Y. V2 (Beta) and 20J/501Y. V3 (Gamma). The survey of VoC Delta started after this study. We therefore included the cumulative proportion of cases infected by one of these VoCs as a potential covariate explaining the transmission. We used data from two cross-sectional "flash" surveys performed on January 7-8, 2021 and January 27, 2021, see Santé Publique France; Santé Publique France and the weekly estimation of VoC circulation provided by the SI-DEP database at the regional level from February 12, 2021 to March 28,2021.
Vaccination process. Vaccination started in France on December 27, 2020. Three COVID vaccines were at that time authorized: BNT162b2 mRNA (Pfizer), ChAdOx1 nCoV-19 (Az-traZeneca) and mRNA-1273 (Moderna). To take into account the vaccination process, we used the VAC-SI database which provides the cumulative percentage of the population who have been vaccinated with at least one dose of vaccine over time. Vaccination was first targeted to the elderly 75+ years of age. The proportion of vaccinated individuals, denoted here within as V , ramped up to about 12% by the end of study period. Representations of the vaccination coverage in the population over time are given in Section 1 of the Web Supplementary Material.
Acknowledgements. The authors thank the opencovid-19 initiative for their contribution in opening the data used in this article. This work is supported in part by  Inria Mission   COVID19 , project  GESTEPID. The authors warmly thank  Jane Heffernan  for scientific discussions and a thorough proof-reading of the article. We also thank  Linda Wittkop ,  Jane Heffernan ,  Quentin Clairon ,  Thomas Ferté  and  Maria Pietro  for constructive discussions about this work.
Furthermore, in a prior study of men with intermediate or high-risk nonmetastatic prostate cancer,  20  only men with no or minimal comorbidity appeared to benefit from the addition of ADT to radiotherapy with respect to OS; those with moderate to severe comorbidity did not. Therefore, using individual patient data from the available RCTs from the Intermediate Clinical Endpoints in Cancer of the Prostate (ICECaP) Consortium database, we evaluated whether adding docetaxel to testosterone suppression plus curative local treatment (radiotherapy or RP) can reduce prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM) in patients with localized or locally advanced nonmetastatic prostate cancer who have both a PSA level of less than 4 ng/mL and a biopsy Gleason score of 8 to 10 and are in otherwise good health with performance status defined as 0.
Patient Population and Treatment Five RCTs that included 2597 patients treated with testosterone suppression plus radiotherapy or RP (SOC treatment) with or without docetaxel in the ICECaP database  13, 14, [16] [17] [18]  were eligible for study inclusion; the data in these studies were updated as reflected in the most recent date of publication. Two RCTs  12, 15  could have been helpful to answer the study question, but their data were not available. Treatment consisted of either RP alone or radiotherapy and ADT for 6 months to 2 years in the SOC group. For patients treated with radiotherapy, 3-dimensional conformal radiotherapy was primarily used, and inclusion of the pelvic lymph nodes in the radiation volume was generally performed for patients with Gleason scores of 8 to 10. In the docetaxel group of the RCT,  17  where RP was the SOC treatment, patients also received 18 to 24 weeks of ADT.
Statistical Analysis Data were analyzed on December 16, 2022. The distributions of the patient, cancer, and clinical factors including self-reported race as per institutional guidelines were enumerated according to randomized treatment group (SOC with or without docetaxel). For the purpose of illustration, the unadjusted distributions of ACM and PCSM were calculated by randomized treatment group using the 1 -Kaplan-Meier method  22  and cumulative incidence function accounting for competing risks,  23  respectively. These distributions were compared across randomized treatment groups using a log-rank  24  P value for the end point of ACM and Gray test 23 P value for the end point of PCSM. To evaluate the treatment effect on the coprimary end points, an HR (with its 95% CI) was estimated for ACM using multivariable Cox proportional hazards regression,  25  and a subdistribution HR (sHR) (with its 95% CI) was estimated for PCSM using multivariable Fine and Gray  26  competing risk regression, with adjustment for performance status (1 vs 0 [reference]), biopsy Gleason score (9 or 10 vs 8), clinical T category (T3-T4 vs T1-T2 or TX), and duration of ADT (2 years [long-term] vs 4 to 6 months [short-term]).
Outputs from living evidence syntheses projects have been used widely during the pandemic by guideline developers to form evidence-based recommendations. However, the needs of different stakeholders cannot be accommodated by solely providing pre-defined non amendable numerical summaries. Stakeholders also need to understand the data and perform their own exploratory analyses. This requires resources, time, statistical expertise, software knowledge as well as relevant clinical expertise to avoid spurious conclusions. To assist them, we created the metaCOVID application which, based on automation processes, facilitates the fast exploration of the data and the conduct of sub-analyses tailored to end-users needs. metaCOVID has been created in R and is freely available as an R-Shiny application.
What this adds • metaCOVID is an online tool that facilitates the exploration and analysis of complex data structures, such as the COVID-NMA database, without requiring technical and programming skills. • This application provides free access to the most up-to-date database for COVID-19 trials for treatment and vaccine comparisons and can assist guideline developers to form timely recommendations. • Using this open-access application, different stakeholders can rapidly investigate the impact of different characteristics on the results and easily produce comprehensive graphical displays. Potential impact for Research Synthesis Methods readers outside the authors' field • This pilot application forms the basis for creating fast and efficient automation tools that accelerate the analyses and the dissemination of the findings for future living evidence synthesis projects.
On the other hand, it is of great importance to reassure that all sub-analyses undertaken are based on clinical rational. It is well-known that the larger the number of subgroup analyses conducted the higher the risk of obtaining false-positive results. 7 A database of more than 400 variables, like the COVID-NMA database, is always subject to this phenomenon and should be used very carefully from investigators with understanding and knowledge of the data. Therefore, within COVID-NMA, only a small number of such secondary analyses are considered; these are pre-defined exploratory analyses included in the COVID-NMA protocol chosen on the basis of the clinical expertise of the steering committee. 1, 2 o address all these important points, we developed and made freely available the "metaCOVID" application (https://covid-nma.
com/metacovid/). This web-application allows the end-users of the COVID-NMA platform and other external researchers to directly use the most up-todate database and perform meta-analyses tailored to their needs in a user-friendly environment. The results of all analyses are summarized in the form of downloadable forest plots. A key feature of the application is that the numerical results are presented alongside study characteristics and risk of bias assessments. In this article, we describe the functionality of metaCOVID and the different available options and sub-analyses it offers. We also discuss how it can inform guideline developers about the impact of difference characteristics on the results using examples from the COVID-NMA data.
| Data embedded in metaCOVID The application is directly connected to the COVID-NMA database which is updated weekly or bi-weekly with data from all new randomized controlled trials (RCTs) evaluating COVID-19 treatments and vaccines. The search is conducted on a daily basis using the L. OVE  8  and the Cochrane COVID-19 Study register platforms. As of March 1, 2022, the COVID-NMA revised its protocol for treatments to update only comparisons evaluating immunomodulators and antiviral therapies. Every RCT in the database is also accompanied by domain-specific RoB assessments (for randomization, missing data, outcome measurement, etc. ) as well as by an overall risk of bias assessment by outcome based on the Cochrane Risk of Bias 2 (RoB 2) tool. 9 In addition, a variety of RCT and population characteristics are being extracted from each trial report including baseline severity, location, type of funding, presence of conflict of interest, and many others.
The COVID-NMA database is separated in two distinct databases which are imported and are continuously updated in metaCOVID: one containing data on all possible treatments against COVID-19 and one on COVID-19 vaccines. In this way, the application always uses the most up-to-date available databases for treatments and vaccines. As of November 2022, the treatment database included 493 RCTs and 522 pharmacologic and nonpharmacologic interventions forming in total 322 pairwise comparisons. Trial populations are separated into hospitalized patients and outpatients. We focus on binary and time-to-event outcomes as well as on both efficacy and safety (Table  1 ). With respect to vaccines, metaCOVID contains all available comparisons between a vaccine and placebo or no vaccination; up to November 2022 data from 117 RCTs on 55 different vaccines were available.
Systemic adverse events Risk ratio COVID-19 vaccines the R packages metafor and meta. 10, 11 The source code of our application is open access through GitHub at https:// github. com/TEvrenoglou/metaCovid 3] [14]  This is because COVID-19 trials are generally heterogeneous in several characteristics and this heterogeneity needs to be taken into account in the synthesis model. Although RCTs with zero events in both intervention arms are excluded from the analysis, they are included in the forest plots for completeness and transparency. For RCTs with zero events in one arm only we use the 0. 5 continuity correction. 12 Of course, the user can optionally change the default settings and select other estimation methods. For example, when the number of available RCTs is small, the REML estimator for heterogeneity may underestimate the true between-study variance.
| RESULTS We illustrate through different examples from the COVID-NMA database how metaCOVID can assist clinicians and guideline developers to explore the impact of different characteristics and modeling approaches on the results of meta-analysis. We chose examples involving well-known treatments and vaccines as well as several studies. For treatments, we use throughout RCTs on hospitalized adult patients for the outcome of all-cause mortality at 28 days as it is probably the most objective outcome. For vaccines, we use an example for the outcome of any adverse events. A tutorial on the use of the application can be found in our Data S1. Figure  1  shows the results for the comparison convalescent plasma compared to standard care or placebo for all-cause mortality. There are 29 RCTs available with hospitalized patients and one of them includes only patients with mild disease.
This application offers open access to the most-up-to date database of COVID-19 RCTs for researchers, clinicians, or guideline developers interested to perform amendable meta-analyses and explore the impact of certain characteristics on the results. Users can download and freely use the outputs from the application given that they recognize metaCOVID and the COVID-NMA platform. A key characteristic of metaCOVID is that it gives the opportunity to the users to undertake fast multiple analyses and present complex data structures without requiring any technical and software knowledge. At the same time, it preserves from arbitrary selections of variables for performing subgroup and sensitivity analyses by following a pre-defined protocol. 1, 2 The living approach implemented in metaCOVID does not apply only to the continuous incorporation of new RCTs but designates all aspects of the systematic review process; namely all considerations regarding data extraction, risk of bias assessments, data synthesis, and appraisal of results are reevaluated regularly based on the evolution of the data and the knowledge for the disease.
Meta-analysis model Pooled The present version of metaCOVID is restricted to the use of the COVID-NMA database. This certainly limits the applicability of the tool to a different setting but future versions will allow the users to upload their data. Also, so far, the databases imported in metaCOVID cannot be downloaded; this will be possible, though, in future updates of application. To avoid large amounts of heterogeneity and misleading conclusions, we have further restricted the application to meta-analyses of RCTs. In this way, possibly important information from observation studies is disregarded. An additional limitation of our application is related to the use of only pre-defined subgroup and sensitivity analyses in the COVID-NMA protocol. However, this reassures that all selected variables have been selected based on scientific rationale and relevant clinical expertise.
Finally, as with all similar tools, the ease that metaCOVID provides for performing complex analyses can also be a drawback if the application is used without cautiousness. Users should be aware that interpretation of meta-analysis results requires understanding of the data and the clinical setting, considerations on the credibility of the evidence, and investigation of all possible sources of within-and across-study biases. 7, 37 Hence, although the application provides a user-friendly environment, it cannot guarantee the proper interpretation of the findings by the users. Our application serves as a pilot version of a generalized application that could be tailored for living metaanalyses of any condition where the users will be able to upload their own data. Although, here the application is linked to the COVID-NMA platform, it can also be seen as a standalone tool since its use does not require any knowledge of the material available in the platform.
AUTHOR CONTRIBUTIONS Theodoros Evrenoglou drafted and revised the manuscript, he programmed the application and he maintains metaCOVID. Isabelle Boutron revised the manuscript and provided clinical expertize regarding the statistical analysis and the data extraction for metaCOVID. Georgios Seitidis revised the manuscript and wrote part of the code. Lina Ghosn revised the manuscript and performed data extraction for metaCOVID. Anna Chaimani drafted and revised the manuscript, wrote part of the code and supervised the construction of the application.
Acknowledgements We thank  Marcin Lis  for raising with two of us (AJ and NB) questions which ultimately triggered our interest in this problem. Part of this work was carried out when AJ visited EA and TL at  NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai. The hospitality of that department is gratefully acknowledged. EA and TL acknowledge the support of the  French National Research Agency (ANR)  within the project  MALIN  ( ANR-16-CE93-0003 ). AJ is recipient of a  DOC Fellowship  of the  Austrian Academy of Sciences at the Faculty of Mathematics of the University of Vienna. AJ's research is partly supported by the  EPSRC  grant  EP/L016516/1  for the  University of Cambridge Centre for Doctoral Training , the  Cambridge Centre for Analysis. NB's work was supported by:  University of Vienna  start-up grant, and  FWF  grant  P33083  on "Scaling limits in random conformal geometry".
Acknowledgements The results of this paper are the result of a collaboration that arose within the  IRC-PHC Ulysses  project " Geometric Constructions of Codes for Secret Sharing Schemes. " The research of the first author was partially supported by the  Irish Research Council , grant n. GOIPD/2020/597. The second author was partially supported by the  ANR-21-CE39-0009 -BARRACUDA  ( French Agence Nationale de la Recherche). The authors express their deep gratitude to  Ferdinando Zullo  and  Julien Lavauzelle  for the.
Acknowledgments. I thank  Semyon Klevtsov  for having introduced me to the quantum Hall effect, and for many stimulating discussions, as the first version of this paper was written during his project "Geometry of quantum Hall states" supported by the  IdEx program  and the  USIAS fellowship of Université de Strasbourg. I would as well express gratitude to  Michele Ancona  and  Yohann Le Floch  for stimulating discussions about probability and complex geometry, and  Raphaël Butez  for several discussions about random matrices and random polynomials. I acknowledge support from  ANR AI chair   BACCARAT  ( ANR-20-CHIA-0002 ).
Acknowledgments The results of this work and its companion [1] were first announced in 2016. Since then, discussions with many colleagues have helped us to find the good formalism. For this we wish to thank  Pablo Barrientos ,  Christian Bonatti ,  Hélène Eynard-Bontemps ,  Victor Kleptsyn ,  Luis Paris ,  Rafael Potrie  and  Maxime Wolff. We also warmly thank the institutions which have welcomed us during these years: the institutions of  Rio de Janeiro PUC ,  IMPA ,  UFF ,  UFRJ ,  UERJ , the  Universidad de la República of Montevideo , the  Institut de Mathématiques de Bourgogne of Dijon , and the  Laboratoire Paul Painlevé of Lille. S. A. was partially supported by  IMPA ,  ANII  via the  Fondo Clemente Estable  (projects  FCE_135352  and  FCE_3_2018_1_148740 ), by  CSIC , via the  Programa de Movilidad e Intercambios Académicos , by the  MathAmSud project RGSD (Rigidity and Geometric Structures in Dynamics)   19-MATH-04 , by the  LIA-IFUM  and by the  Distinguished Professor Fellowship of FSMP.