dataesr/scientific-paragraphs-categorization huggingface dataset:
The second level represented the medical technicality of the hospital using qualitative variables. The status of the hospital (private/public), the level of medical technicality (presence or absence of a triage nurse on admission, the number of hospital beds and the number of full-time equivalent (FTE) physicians or nurses at the ED, the proportion of long-and short-term hospitalisation and the number of Emergency Mobile Services (EMS) transports were extracted from the ORU and the DREES database. We extracted the number of admissions and length of stay in ER from the ORU, the presence or absence of percutaneous coronary intervention center, stroke unit, resuscitation unit, intensive care unit, psychiatric services, and emergency medical service from the regional health agency (ARS). The third level represented patient characteristics with data extracted from the ORU such as the distribution of patients by age and sex, the proportion of hospitalisations consecutive to the ED visit, and the severity using the French clinical classification of patients in the emergency department (CCMU) which evaluates the patient's condition in the emergency department, their level of clinical severity, and their medical prognosis (from 1-stable clinical conditions to 5 -life-threatening).
Method selection The high dimensionality and collinearity between these three input levels make standard regression techniques prone to unstable results and difficulties in generalization. Among the mathematical techniques for describing the underlying patterns in complex and correlated data sets, we choose the multiple factor analysis (MFA). This method allowed us to take advantage of the multilevel (territory, ED, patient) structure  [20]. MFA is part of a family of multivariate statistical methods that aimed to identify the combination of variables that explains the greatest variability in a dataset. The most important variables are then reorganized into new synthetic variables, called axes, which are a linear combination of the original input variables. These new synthetic variables are numeric, centred around zero.
Data management We provided imputations on missing values when these did not exceed the 20% threshold. Above this threshold, the proportion of missing data was considered too large to cover the missing values, and the decision was made to exclude these variables from the study. We also excluded 4 EDs with too much missing data. The Multiple Imputation by Chained Equations (MICE) method, based on a Monte-Carlo Markov Chain algorithm, was used to generate an imputed dataset. We quantified the territorial and demographic data from the raw data and produced ratios. For example, the number of general practitioners relative to the population residing in the health zone. A constraint of MFA is to have data of the same type within each level. Some of the medical technicality level data were categorical, so we created categorical ordinal data for characterizing the structure's medical technicality into groups.
For example, the number of visits per structure was divided down into 4 classes; from 0 to 15,000, from 15,000 to 30,000, from 30,000 to 45,000, then above 45,000. When no a priori classifications were available, the continuous variables were divided into 5 classes using the "arules" package cluster method. After imputations, we retained 73 EDs in this study (93. 6% of all EDs).
Statistical analysis We provided a description of the ED groups using the median and interquartile range of input at three levels as well as frequency and percentages. Statistical analyses were performed using R 3. 6. 1 software with the "mice" package for imputations  [23] , "FactoMineR" for running MFA methodology  [24] , "factoextra" for cluster selection  [25] , "arules" for discretizing variables  [26]  and "cluster" for kmedoid clustering  [27].
Discussion We proposed an automated and reproducible multi-level approach to understand the underlying typology of EDs in our region. Based on input variables at three levels, this approach considered 1) the territories and the surrounding health care supply, 2) the profile of patients admitted to ED and 3) the hospital activity with its technical specificities. One of its particularities is to not take into account any prior hypothesis. The MFA methodology has been used and published in different search fields  [21, 28, 29]. But the objectives were identical: to describe patterns in order to establish typologies. These applications illustrate that this approach does not only belong to the statistics but consists in extracting knowledge from structured datasets. In this study, the low variance explained and evaluated on the first three axes (about 33.
Acknowledgments The authors thank the  ORU ARA group , constituted by  Guillaume DEBATY  ( CHU Grenoble ),  Nicolas DESSEIGNE  ( CHU St-Etienne ),  Jonathan DUCHENNE  ( CH Aurillac, RAMU), Carlos EL KHOURY (Médipôle Lyon-Villeurbanne ),  Laurie FRATICELLI  ( RESCUe-RESUVal network, Laboratory Systemic Health Care EA 4129 ),  Julie FREYSSENGE (RESCUe-RESUVal network ,  HESPER EA 7425 ),  Maxime MAIGNAN  ( CHU Grenoble ),  Mikaël MARTINEZ  ( CH Forez REULIAN ),  Fares MOUSTAFA  ( CHU Clermont-Ferrand ),  Véronique POTINET  (HCL),  Abdesslam REDJALINE  ( CH Firminy ORU ARA ),  Dominique SAVARY  ( CH Annecy ),  Jeannot SCHMIDT  ( CHU Clermont-Ferrand ), Karim TAZAROURTE (HCL),  Cécile VALLOT  ( CH Annecy, RENAU ),  Alain VIALLON  ( CHU St-Etienne ). The authors also thank  Marie LASSAIGNE ,  Doriane ARGAUD ,  Bruno FERROUD-PLATTET  and  Delphine CASAMATTA  from the regional agency for health  Auvergne Rhône-Alpes , and  Emeline MODERNI  for the English proofreading.
In the present study, we included participants who accessed the survey in France between 21 April and 7 June 2020, corresponding to 141 individuals (39. 9% of persons who accessed the survey from France). MAKASI is a community-based interventional study, which includes migrants from sub-Saharan Africa in the Greater Paris area experiencing social disadvantage, to assess the effectiveness of an empowerment intervention on risks in terms of sexual health. The research protocol includes a baseline questionnaire on participants' socioeconomic circumstances and mental health, as well as follow-up questionnaires 3 and 6 months after intervention delivery. Questionnaires were completed in French or in English. The study was registered with Clinical Trials: NCT04468724  (12). At the time of the first lockdown and owing to the established protocol, we continued this follow-up by phone and added questions specific to the COVID-19 pandemic and lockdown situation.
Statistical analyses In the three studies, we used data collected between the 1st of April and 7th of June 2020. To test the association between participants' legal status and their living circumstances as well as level of knowledge of COVID-19 and practice of preventive measures, we first described the sociodemographic characteristics of the three study samples, and we then tested associations between the eight outcomes and participants' legal status (undocumented or not) using chi-square tests. Thirdly, we modelled the probability of food insecurity increase, job loss, depression, and responses to SARS-COV-2 symptoms with logistic regression models, adjusting for age, gender and legal status. All analyses were stratified by survey and conducted using SPSS (APART TOGETHER), R (ECHO) and STATA (MAKASI).
Acknowledgements APART TOGETHER. The  Apart Together Sudy group in included Gesine Sturm ,  Rachid Oulahal ,  Filipe Soto Galindo ,  Julia de Freitas Girardi  and  Yagmour Gökduman. The authors also thank the entire  APART TOGETHER academic consortium , especially  Ilse Derluyn ,  An Verelst ,  Eva Spiritus-Beerden  and  Morten Skovdal  as well as the  WHO  for support and dissemination of the study. A special thanks to all participants as well as to our partners and professionals who helped to communicate the survey to participants of hard to reach groups.
Funding information The MAKASI study was supported by the  French National Agency  for research on AIDS and Viral hepatitis (ANRS) and  Health regional agency (Agence régionale de santé Ile-de-France) , as well as  Université de Paris  ( ANR-18-IDEX-001 ). The  ECHO study was supported by l'Institut Convergences MIGRATIONS/CNRS  ( French Collaborative Institute on Migrations ), ( ANR-17-CONV-0001 ), by  Santé Publique France  and  ANR   Flash-Covid 19.
Thus, new preventive strategies using pre/probiotics are emerging  [260]. Numerous studies on the oral microbiota allowed scientists and mathematicians to generate a large database that can lead to the identification of new probiotics from oral microbiota in metabolic regulation. Already, specific probiotics such as Bifidobacterium pseudocatenulatum and Bifidobacterium catenulatum are used to treat liver damage by attenuating D-galactosamine  [261]. Also, in mice, treatment with the probiotic Bifidobacterium pseudocatenulatum reduces obesity and inflammation by improving the epithelial barrier of the oral cavity  [262, 263]. In the oral cavity, guided periodontal pocket recolonization associated with periodontal non-surgical treatment reduced local inflammation and improved long-term clinical outcomes  [264].
Data sources We included industrialised countries with complete or near-complete registration of deaths in our analysis if: • Their total population in 2020 was more than 100,000. We excluded countries (e. g. , Liechtenstein) with data but with smaller populations because, in many weeks, the number of deaths would be small or zero. This would, in turn, lead to either large uncertainty that would make it hard to differentiate between those places with and without an effect or unstable estimates because the model is fitted to many weeks with zero deaths. • We could access up-to-date weekly data on allcause mortality divided by age group and/or sex that extended through February 2021. • The time series of data went back at least to the beginning of 2016 so that model parameters could be reliably estimated.
For countries with longer time series, we used data starting in 2010. The 40 countries in our analysis were divided into five geographical regions: the Pacific (Australia, New Zealand, South Korea), the Americas (Canada, Chile, the USA), Central and Eastern Europe (Austria, Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Montenegro, Poland, Romania, Serbia, Slovakia, Slovenia), Southwestern Europe (Cyprus, France, Greece, Italy, Malta, Portugal, Spain), Northwestern Europe (Belgium, England and Wales, Germany, Luxembourg, the Netherlands, Northern Ireland, Scotland, Switzerland) and Nordic (Denmark, Finland, Iceland, Norway, Sweden). In addition to national estimates, we separately estimated excess deaths for all 50 US states and the District of Columbia, some of which are larger than most other countries included in our analysis, because the extent and temporal dynamics of the pandemic were heterogeneous across states.
The sources of population and mortality data are provided in Table  1. We calculated weekly population through interpolation of yearly population, consistent with the approach taken by national statistical offices for intra-annual population calculation  6. Population for 2020 and 2021, where not available, was obtained through linear extrapolation from the last five years. We obtained data on temperature from ERA5  7  , which uses data from global in situ and satellite measurements to generate a worldwide meteorological dataset, with full space and time coverage over our analysis period. We used gridded temperature estimates measured four times daily at a resolution of 30 km to generate weekly temperatures for each first-level administrative region, and gridded population data to generate population estimates by first-level administrative region in each country.
Sex-specific analysis (see • Finally, death rates may be different around major holidays such as Christmas and New Year either because of changes in human activities and behaviour or, for the countries whose data are registration based, because of delays in registration. We included effects (as fixed intercepts) for the weeks containing Christmas and New Year in all countries. For England and Wales, Scotland and Northern Ireland, we also included effects for the week containing other public holidays, because reported death rates in weeks that contain a holiday were different from other weeks. This term was tested but not included for other countries because the effect was negligible. These choices led to an ensemble of 16 Bayesian models (2 yearly autoregressive options × 4 weekly autoregressive options × 2 temperature anomaly options).
It consists of 52 terms with an independent and identically distributed prior defined via ν week of year ~ (0, Finally, the term ε week is a zero-mean term that accounts for additional variability. It is assigned an independent and identically distributed prior ε week ~ (0, 2 ε σ ), and a logGamma(0. 001,0. 001) prior was placed on the log precision log(1/ 2 ε σ ). The components α 0 ,α holiday(week) , β⋅week, θ week , ε week and ζ ( ) week i (for autoregressive order i = 1,2,4 or 8) appear in the expression for log(death rate week ) in all models. The remaining components appear in some models only. Table  2  shows the terms included in each of the 16 models in the ensemble. We used data on weekly deaths from the start of time series through mid-February 2020 to estimate the parameters of each model, which were then used to predict death rates for the subsequent 52 weeks as estimates of the counterfactual death rates if the pandemic had not occurred.
These were then compared to reported deaths to calculate excess mortality due to the pandemic. For the projection period, we used recorded temperature so that our projections take into consideration actual temperature in 2020-2021. This choice of training and prediction periods assumes that the number of deaths that are directly or indirectly related to the COVID-19 pandemic was negligible through mid-February 2020 in these countries  1  , and separates the training data from subsequent weeks when impacts may have appeared. All models were fitted using integrated nested Laplace approximation (INLA)  23  , implemented in the R-INLA software (version 20. 03). We used a model averaging approach to combine the predictions from the 16 models in the ensemble  24, 25. Specifically, we took 2,000 draws from the posterior distribution of predicted deaths under each of the 16 models, and pooled the 32,000 draws to obtain the posterior distribution of deaths if the COVID-19 pandemic had not taken place.
The mean relative absolute error was between 8. 0% and 8. 7% in different years. 95% coverage, which measures how well the posterior distributions of projected deaths coincide with withheld data was 96% for all years, which shows that the posterior distribution is well estimated. An earlier version of this manuscript can be found on medRxiv (doi: https://doi. org/10. 1101/2021. 07. 12. 21260387).
Comparison with other estimates Financial Times and The Economist's excess deaths tracker report the number of excess deaths for various countries based on comparisons of deaths in 2020 and 2021 with 2015-2019 averages. This approach does not account for general trends in mortality nor for factors like temperature that affect mortality and vary from year to year. The Economist has also recently published a set of excess deaths estimates using data from the Human Mortality Database and the World Mortality Dataset 61 , and an ensemble of gradient boosted decision trees. Countries with small, medium and large number of excess deaths are largely consistent between our analysis and these sources. There are nonetheless some differences. For example, we estimated ~76,100 excess deaths for Spain, compared to ~88,900 by Financial Times and ~85,500 by The Economist.
Temperature was included in our model because it is an established predictor of short-term mortality, and was itself not affected by the pandemic. We did not include air pollution in the model, because the pandemic and the associated policy and behavioural responses, in particular the reduction in vehicular traffic and industrial activity, led to changes in air pollution  1, 2. This in turn means that the observed pollution is different from what pollution levels would have been without the pandemic. In contrast, researchers have estimated a short-term pandemic impact of only ~0. 01°C on ambient temperature 3 , which is orders of magnitude smaller than seasonal and interannual variation. The age distribution of each country is already embedded in past data on deaths and population, and is therefore implicitly used to produce our projections.
Data availability Underlying data Input data on deaths, population and temperature are available at https://doi. org/10. 5281/zenodo. 5535829 8. This repository contains the following underlying data: • data/data. csv (data on deaths, temperature and population by age group, sex, country and week) • output/result_summaries. csv (weekly estimates of predicted deaths, excess deaths, excess death rates per 100,000 and relative increase in deaths) Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1. 0 Public domain dedication). The original data sets used in the study are publicly available from the following locations: Data on deaths and population UN World Population prospects: https://population. un. org/ wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/ Data on temperature and gridded population https://www.
ecmwf. int/en/forecasts/datasets/reanalysis-datasets/ era5 and https://sedac. ciesin. columbia. edu/data/collection/gpw-v4 Extended data The computer code for the Bayesian model ensemble used in this study is available at: https://github. com/vkontis/excess_mortality/ tree/pub2 Archived analysis code at time of publication: https://doi. org/10. 5281/zenodo. 5535829 8. License: GNU Affero General Public License v3. 0.
24,000 (18,500 to 29,300) 19,777 Denmark 2,400 (-170 to 5,100) 2,343 England and Wales 102,100 (75,300 to 128,600) 128,077 Estonia 790 (140 to 1,500) 535 1 Data are from Office for National Statistics for England and Wales (https://www. ons. gov. uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/ weeklyprovisionalfiguresondeathsregisteredinenglandandwales), NRS for Scotland (https://www. nrscotland. gov. uk/covid19stats),NISRA for Northern Ireland (https://www. nisra. gov. uk/system/files/statistics/Weekly_Deaths%20-%20w%20e%203rd%20September%202021. XLSX) and the European Centre for Disease Prevention and Control (ECDC) for other countries (https:// opendata. ecdc. europa. eu/covid19/nationalcasedeath/csv;accessed on 21 September 2021). Figure 1. Number of excess deaths due to the first year of the coronavirus disease 2019 (COVID-19) pandemic by country.
Clinical Assessments Clinical assessments are performed at inclusion and during follow up (Table  1 ). Except for the FTSS test, which is applied during follow up and at the end of the study, all clinical assessments are carried out as part of usual care. Clinical data are collected from the computerized patient medical file or directly from the participant if the data have not yet been added to the file. The clinical assessment at inclusion will include the reason for hospitalization (including organ failure, musculoskeletal disorders or falls, neuropsychiatric disorders, medicosocial problems, or other causes); age and gender; anthropometric data (ie, weight and height); body mass index (kg/m 2 ); and the Mini-Mental State Examination (MMSE)  [15]  score. The MMSE is used to assess cognitive function.
To date, 266 inpatients have been enrolled. We have not analyzed the results, but our initial observations suggest that the usability of each type of exercise for the patients depends on their fall risk level, and that the program has not been perceived as difficult or time consuming by the health care team. Recently published meta-analyses describing the role of physical activity in preventing falls by patients in posttreatment and rehabilitation wards first reported the effectiveness of exercise programs in 2012  [7] , then showed less certainty in 2018  [18]. These meta-analyses also highlighted the fact that there have been very few studies conducted in geriatric acute-care units and that the data were divergent, with some studies showing efficacy and others not. The recent SPRINTT (Sarcopenia and Physical Frailty in Older People: Multi-Component Treatment Strategies) study  [19]  was a randomized controlled trial aimed, in part, at testing a physical activity intervention that appeared comprehensive (including aerobic endurance, gait, balance, flexibility, and resistance exercises) and monitored the intensity of the exercises to protect the frail, older participants.
BACKGROUND It remains unclear which patients with metastatic germ cell tumours (mGCTs) need prophylactic anticoagulation to prevent venous thromboembolic events (VTEs). OBJECTIVE To assess the risk and onset of VTEs stratified by risk factors. DESIGN, SETTING, AND PARTICI-PANTS This multi-institutional retrospective dataset included mGCT patients treated with first-line platinum-based chemotherapy. INTERVENTION Patients with prophylactic anticoagulation were excluded. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS A regression analysis was performed to select risk factors for VTEs. The simulated number needed to treat (NNT) and the number needed to harm (NNH) with prophylactic anticoagulation were calculated based on the cumulative incidences retrieved from this study and hazard rates of recently published trials describing the efficacy of prophylactic anticoagulation to prevent VTEs and the risk of bleeding events.
Performance should be assessed with another scale: a-activity performed alone; b-activity partially performed with human assistance; c-activity performed with continued assistance; and d-activity not performed. This performance scale provides the information about the environment, especially mentioning the potential impact of human assistance on the performed activity. This means that conceptually the data that might be collected at that stage should contain one related to D and one related to E. The following general five types of E are required to be documented as facilitators or barriers during the performance assessment: human environment [ICF chapter e3 Support and relationships]; technical assistance [e1 Products and technology] housing    to know whether they have an impact on the individual situation, as a facilitator or an obstacle.
Five models were tested: with centre and experimenter as random effects, with centre and experimenter as nested random effects, only with centre, only with the experimenter, and without any random effect. The model with the lower AICc was kept  [68]. As the second step, we applied an automatic procedure based on the AICc (Package MuMIn)  [69]  on the previously chosen model to select which fixed effect to include in the model. The procedure tested all possible combinations of fixed effects and ranked the worst to the best fitting model. This procedure ensures that only significant factors are kept in the model. As a third step, all possible interactions were tested from the model chosen in the second step. The AICc was recalculated, and the interaction was only retained in the model if the AICc decreased by more than 2 points; this procedure ensures that only significant interactions are kept in the model.