dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods To determine the type of interprofessional collaboration to prevent adverse drug event in which a pharmacist was involved, we conducted a systematic review of the literature of randomized controlled trials. Two independent reviewers screened trials in three databases: Medline, Web of Science, ScienceDirect. Prospective studies of at least three different health professionals' interventions, one of whom was a pharmacist in the last ve years were included. Two reviewers performed data extraction and quality appraisal independently. We used TIDieR checklist to appraise articles quality.
Data collection process, quality, and risk of bias assessment Selected citations were uploaded to a Zotero database. Study data were collected and managed using REDCap electronic data capture tools  (29, 30). Data of selected studies were analyzed using TIDIeR checklist (Template for Intervention Description and Replication)  (31). Each item on the assessment is worth one point. Studies with a score of less than 9 out of 12 points have been excluded from the systematic review.
In the Care Ecosytem intervention, community health worker connected the different health care professionals with each other and with the patient under nurse supervision  (35). In Possin et al study, community health educator was a pivotal person who provided a link between the patient or caregiver and specialized health professionals  (35). Interventions typically involved several steps (Fig. 2 ). The rst is to collect clinical, biological, pharmaceutical, or social data relevant to the analysis. Most of the time, this research was done by the nurse, pharmacist, or a community health educator. Assessment questionnaires were lled in either by the health professional or by the patient himself. For example, in GAIN intervention, patient and health professionals complete an in-depth geriatric assessment.
The prevalence of common disease conditions and causes of death have mostly been reported for specific conditions and in a few recent large cohorts of IDS or population studies not taking healthcare use into account  14, [20] [21] [22]. Hospital diagnoses over the individual's lifespan are not detailed for chronic and acute diagnoses  [22] [23] [24]. Comparisons of the annual use of various healthcare is possible with the SNDS (the French national health database), which provides information about health conditions and healthcare reimbursements almost the entire French population; given that health insurance coverage is universal in France  25. We performed the first national observational study comparing a large population of IDS to individuals without Down's syndrome (IWDS) over 2019. This study focused on DS prevalence, social and demographic characteristics, comorbid conditions and mortality.
Data source The SNDS collects individual information from the various insurance schemes, the types of healthcare used and the amounts reimbursed  25. It does not record diagnoses made in non-hospital settings or the results of clinical examinations and investigations. However, it does include information about long-term disease (LTD) status for a specific condition, which justifies 100% reimbursement of specific healthcare expenditures for at least five years, if requested by the patient's general practitioner (GP). The list of LTDs is published by decree. A pseudonymized identification number is used to link above information with data from the national hospital discharge database concerning stays in public and private hospitals: short-stay hospitalization (SSH), hospitalization at home, or hospitalization in psychiatric hospitals and rehabilitation facilities.
LTD and hospital diagnosis are coded using the International Classification of Diseases 10th revision (ICD-10). Deaths are also collected in the SNDS. For comorbidities, we also used data from "Healthcare Expenditures and Conditions Mapping", a tool based on the SNDS developed for the analysis and monitoring of disease burden through evaluations of healthcare use  28. This tool can be used to identify 58 diseases or chapter of diseases through the use of medical algorithms based on the reasons for hospitalization, long-term disease diagnoses and the reimbursement of specific treatments for certain diseases, for a given year and a period up to four years preceding that year. For tumors, exclusive algorithms identified different states of the same disease related to active treatment or acute care or to long-term follow-up and chronicity or under surveillance.
Data availability All SNDS data are pseudonymised and individually linkable. The sharing of these data is expressly forbidden by law (sensitive individual data). Access to data is provided subject to prior training and authorization and must be approved by the French independent data protection authority (CNIL, "Commission Nationale Informatique et Libertés"). Data are available after request from the Health Data Hub (HDH contact via hdh@health-datahub. fr) for researchers (public or private) meeting the criteria for access to confidential data. Thus, data that support the findings of this study are publicly available after specific request and authorization  25.
Of course, this does not mean that these short tests are appropriate for use as a brief instrument to identify older adults at risk of future functional decline, and the study findings suggest that more work is needed to identify such a tool. Another interesting point raised by the paper is that work to date on the IC construct has generally adopted a reflective rather than a formative approach  (3, 4). This can largely be explained by the purpose of much of this previous work, which was not aimed to develop a summary measurement tool (i. e. no scoring algorithm was proposed) but to examine the intercorrelation between a range of variables within existing datasets to explore how intrinsic capacity might be structured. While there is general agreement that intrinsic capacity can be considered a latent construct that emerges from complex underlying characteristics, it is still unclear whether it is best assessed as a formative or reflective construct.
Data recorded For all residents, we recorded the specialty that they were pursuing (general [family] medicine; emergency medicine or pediatrics); their semester of residency (from 1 to 8 or 10 depending on the specialty; the first semester starting after 6 years of medical school); and their sex. We dichotomized the cohort according to specialty (general [family] medicine vs. emergency/pediatrics specialists), and according to the number of semesters of residency (semesters 1-3 were classed as junior residents; and semesters 4 and above were classed as senior residents, as having more experience). We also separated the group according to whether they were on call during the first 5 months of the semester (November 2021 to March 2022) or in the sixth and final month of the semester (April 2022).
10 on univariable analysis. For each multivariable model, we adjusted for the number of patients seen while on call, and included the subject as a random effect. Results are presented as odds ratios (OR) and 95 % confidence intervals (95 % CI). A value of p < 0. 05 was considered statistically significant. All analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC).
Acknowledgments CP thanks all the residents and pediatricians who participated in the study;  Mohamed Amir Moussa , who created a database with Qualtrics; the secretaries from the three pediatric departments, who helped with the data collection;  Rachel Reinert  and  Marie Pietrement  for their help in the data recording;  Dr. Yannick Plenier  and  Dr. St ephane Sanchez  for the data extraction from the software of the pediatric emergency departments;  Prof. Thierry Pelaccia  for the administrative part of the study; and the  American Memorial Hospital Committee  for its support.
Methods: In this meta-analysis on adult cancer care, we excluded randomised controlled trials, and studies of survivors without active disease or involving analogue patients. Eight databases were searched, in addition to reference lists of relevant articles and grey literature. Two reviewers independently screened citations, extracted data, assessed risk of bias and graded quality of evidence by using the AXIS tool. Effect size correlations (ESr) were chosen and pooled by using a random effect model. Subgroup analyses were performed, and statistically significant variables were introduced in a meta-regression. Several methods were used to explore heterogeneity and publication biases.
3. Investigated physician empathy (surgeon, oncologist, and any medical specialist that patients met for their cancer care). 4. Involved quantitative research. 5. Assessed the association of PE with one or several PO. Outcomes could be defined as the changes that result from health care. Studies were excluded on the basis of the following exclusion criteria: 1. Studies about (a) survivors who no longer have cancer or (b) literature reviews and meta-analyses, as the data did not allow us to perform our analyses. However, their references were screened. 2. Studies about nurses or allied healthcare professionals exclusively. 3. Studies about primary care physicians, because the lack of coordination of cancer care between hospitals and community physicians sometimes makes it difficult for them to fully support their patients on their cancer care journey.
| Information sources and search The databases MEDLINE, PsycINFO, Academic Search Premier, Scopus, PsycARTICLES, Web of Science, Cochrane Library and Open Grey were searched. The following limiters were applied when they were available: English/French language, human studies, adult population, abstract available, peer-reviewed articles. Articles from 1 January 1990, up to 10 November 2022, were extracted. Reference lists of retained and relevant studies were hand searched.
| Data collection, extraction and management A list of search terms was developed according to the literature. Different combinations of search terms were tested before extraction. The search strategy is available in Appendix A. Titles and abstracts of the retrieved studies from the search strategy and those from additional sources were screened independently by two authors (Lucie Gehenne and Christelle Duprez) to identify studies that met the inclusion criteria. The full texts of these eligible studies were retrieved and independently assessed for final inclusion by two team members (Lucie Gehenne and Christelle Duprez). Disagreements were discussed with one of the other two authors (Sophie Lelorain and Véronique Christophe). A standardised, pre-piloted form was used to extract data from the included studies for assessment of study quality, evidence synthesis and data.
This pre-piloted form was edited, validated by the other two authors (Sophie Lelorain and Véronique Christophe), and tested on 5% of studies. After it was considered satisfactory, the following data were extracted: information about the report ( The evaluation of the quality of studies and risk of bias was assessed by using the 20-item AXIS tool,  21  one of the rare available tools to assess the quality and risk of bias of observational studies. For each item, the answers are yes, no, don't know/comment. The quality of studies was independently coded by two authors (Lucie Gehenne and Christelle Duprez) and discussed with one of the other two authors (Véronique Christophe and Sophie Lelorain) to reach consensus. A score out of 20 was calculated for each article.
| Analyses Correlation was chosen as the effect size (ESr). A negative value indicates an unfavourable association between PE and PO (e. g. , PE is associated with higher patient anxiety), whereas a positive value indicates a favourable outcome (e. g. , PE is associated with higher patient satisfaction). When ESr was not directly available from studies, other ES were retrieved and transformed into Fisher's Z by Comprehensive Meta-Analysis (CMA) software. When linear standardised coefficients were available, they were transformed into correlations by using the formula by Peterson and Brown. 22 Even though the method may not have been most appropriate for high ES, we used it because high ES are rare in the field and it is by far the most convenient method among those available. 23 All choices and computations of ESr are explained in Appendix B.
The IPSE analytic process is a rigorous procedure that relies on an inductive, phenomenological method. In practice, the analysis had two stages: one stage of independent work by two researchers, aided by Nvivo 12 software, and the other by the group, pooling the data collectively. In the individual procedure, the three qualitative researchers independently and simultaneously conducted a systematic descriptive analysis to convey each participant's experience. This involved for each interview: (i) listening to the recorded interview twice and reading it three times; (ii) exploring the experience word by word, that is, cutting up the entire text into descriptive units; (iii) regrouping the descriptive units into categories. These stages are carried out with the help of QSR NVivo 12 software.
Results The study included 20 students (16 women, 4 men). No students that agreed initially to participate to the study dropped out. Table  4  summarizes their characteristics. Their ages ranged from 19 to 26 years; 12 were in medical school, 5 in pharmacy and 2 in nursing sciences and 1 physiotherapy. The interview took place between 1 month and 9 months after the 2-day training. Data analysis produced a common structure of lived experience based on three central axes of experience: (1) a personal experience; (2) a student experience; and (3) a professional experience (Figure  1 ). Transcript excerpts presented below have been selected to exemplify the themes described and translated into English for the sole purpose of this article.
Introduction: Despite the progress in the management of the pandemic caused by COVID-, it is necessary to continue exploring and explaining how this situation a ected the athlete population around the world to improve their circumstances and reduce the negative impact of changes in their lifestyle conditions that were necessitated due to the pandemic. The aim of this study was to analyze the moderating influence of physical activity (PA) and dietary habits on the impact of the COVID-pandemic experience on sleep quality in elite and amateur athletes. Materials and methods: A total of , elite (. %) and amateur (. %) athletes ( % women; % men) from di erent countries participated in a cross-sectional design study. Data were collected using a battery of questionnaires that identified sociodemographic data, sleep quality index, PA levels, dietary habits, and the athletes' perception of their experience during the COVID-pandemic.
Procedure The study was carried out 2 years after the formal declaration of the SARS-CoV-2 illness (COVID-19) as a pandemic by the World Health Organization. A survey assessing the impact of COVID-19 on sleep habits, level of PA, and dietary intake was conducted over 2 months (March and April 2022). Participants responded to the survey in their native language in an online format via Google Forms. Informed consent to participate in the study was obtained by selecting a "check box. " A back translation method (42) was employed to translate the survey from English to Arabic, French, German, Persian, Portuguese, Spanish, and Turkish languages. For the PSQI and REAP-S, the existing translated versions were applied. The research followed the ethical recommendations for the treatment of subjects and data obtained as stated in the Declaration of Helsinki  (43)  and safeguarded the integrity and respected the anonymity of each participant's answers.
Statistical analysis The statistical analysis was performed using SPSS v25. 0 (SPSS Inc. , Chicago, IL, USA). Data were presented as mean ± SDs and mean rank in the tables and the text. The Kolmogorov-Smirnov (KS) statistic was used to determine if a sample came from a population with a specific distribution. To analyze the differences between the social characteristics of amateur and elite athletes, we used the chi-squared test. The Mann-Whitney Utest and the Wilcoxon test were used to compare the differences between amateur and elite athletes before and during COVID-19. A Rho Spearman-Brown correlation analysis was employed to determine the relationships among the coronavirus pandemic experience (stressful, challenging, controllable, and threatening), sleep quality, PA level, and eating behaviors.
For all the analyses, significance was set at a p-value of < 0. 05. Finally, two independent simple moderation analyses (Model 1) were evaluated to determine the moderating roles of PA and dietary habits on the interaction between the pandemic experience and sleep quality. The PROCESS V. 3. 5 macro (44) extension installed in SPSS v. 25 software was used. Confidence intervals (95%) were generated by bootstrapping for 10,000 samples to determine the three model effects (b1, b2, and b3). Mean ± SDs were employed to divide the PA and dietary habits levels in the moderation analysis.
Notwithstanding the foregoing, the PA levels were lower during the COVID-19 pandemic period than before for both elite and amateur athletes, even when the data for the present study were collected 2 years after the pandemic was declared. This result may be due to the period when the data were collected, as the pandemic was not over and continued social restrictions and coronavirus-related anxiety may have limited the return to normal sports practice (25). In addition, the variable PA showed a positive and significant correlation with REAP-S. Whereas, PA had a negative association (p < 0. 05) with the perception of COVID-19 as stressful, challenging, and threatening. A few previous studies have reported a positive relationship between PA and eating behaviors, both during the pandemic period  (1, 60, 61)  and before the pandemic  (50, 52, 62).
Search strategy We considered three bibliographic databases, namely Web of Science, Science Direct and PubMed, to collect academic research papers that: (1) were published online until January 2023; (2) contained at least one of the identified terms or keywords in either the abstract, title and keywords; (3) were published in journals, conference proceedings or book series; (4) were written in the English and French languages. The search terms (keywords) are listed in Fig. 1. We used five sets of keyword terms: HHC settings, planning, type of decision, uncertainty and method. We combined these keywords with the Boolean operator 'OR' and 'AND' to filter relevant papers.
Based on this selection approach, the number of papers that are included or excluded are shown in Fig. 2  according to the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) flow chart. Reasons for the exclusion of texts at full text assessment are shown also in Fig. 2. At the beginning we identified in total 85 papers, 20 from these papers were duplicates so they were removed. The 67 remaining papers went through the screening phase. 7 papers were not retained as they met exclusion criteria as follows: 3 papers are excluded due to exclusion criteria E1, E2 and E3, 1 due to only E1, 1 due to E2 and 2 due to E3 respectively. In the end, 50 papers out of potential ones entered the data collection stage for qualitative and quantitative analysis. Based on the electronic databases, we note that, among the 50 included papers, 41% appeared in Science Direct reference database, 36,2% in web of science reference database and 4,44% in Pubmed database.
Data extraction For each included paper, two types of information were extracted and entered the database. 1. basic data about the papers: a. author, paper titles, date, country, type of paper b. paper keyword, topic (research question) c. their source-based categories (e. g. , from a journal, book or a conference) and related information. 2. Content-related data: The type of planning decision, the length/ type of planning horizon, the objectives or performance measures, the constraints, the modelling methods and solution approaches, the type of uncertainty if considered.
5mL of pre-reduced PBS with 40mM cysteine (Millipore-Sigma) as a reducing agent. Tubes were weighed after adding material and transferred into a Coy anaerobic chamber (Coy Labs) at 37°C for serial dilutions with plating to selective C. difficile CHROMID agar (Biomérieux) or Brucella agar (Becton Dickinson) for commensal quantitation. C. difficile colonies were counted at 48 hours of incubation. C. difficile spore preparations and counts were defined by exposing pre-weighed material to 50% ethanol for 60 minutes followed, by serial dilution and plating to C. difficile CHROMID agar. Vegetative cell biomass was calculated by subtracting the spore biomass from the total biomass and normalizing to the cecal mass. Plasmid and strain construction. All primers and plasmids used are listed in Extended Data Table  1  and 2.
RNA (1µg) was incubated 10 min at 70°C with 1µg of hexamer oligonucleotide primers (p(DN)6, Roche) before adding 10 µl of 5X AMV RT buffer, 4 µl of dNTP (25mM), 1µl of RNasine (Promega) and 1µl of AMV RT enzyme (Promega) for a 50µl reaction volume. After 2h incubation at 37°C, the reaction was stopped by heat treatment (85°C) during 5 min. Real-time quantitative PCR was performed in 20μl reaction containing 20 ng of cDNAs, 10 μl of the SYBR PCR master mix (Life Technologies, Fisher) and 400 nM of tcdA, tcdB, or tcdR gene-specific primers (Extended Data Table  2 ). Amplification and detection were performed using a StepOne TM instrument (Applied Biosystem). For each sample, the quantity of cDNAs of target genes was normalized to the quantity of cDNAs of the DNA polymerase III gene (dnaF). The relative change in gene expression was recorded as the ratio of normalized target concentrations (threshold cycle [ΔΔCT] method)  37  Toxin ELISA.
Acknowledgments: This work was funded by the  Institut Pasteur  and the  "Integrative Biology of Emerging Infectious Diseases" (LabEX IBEID)  funded in the framework of the  French Government 's " Programme Investissements d'Avenir"  to B. D, the  ANR-20-CE15-0003  (Difficross) to J. P, and grants  R01AI153605 ,  R03AI174158 , and  P30DK34854  from the  National Institute of Health in the United States , and a capital grant from the  Massachusetts Life Science Center (MLSC)  to LB.
, 2012). It is tempting to speculate that GM represents one of these carry-over effects and that exposure to environmental bacteria along migration routes and at wintering grounds could result in divergent GM structures between resident and migratory species during the breeding season. Alternatively, GM variation between migrating and resident species could be caused by ecological and physiological adaptations evolved as a consequence of migration behavior  (Piersma et al. , 1999; McWilliams and Karasov, 2001; Schwilch et al. , 2002; Owen and Moore, 2008). Previous studies have already shown that avian GM can vary between breeding and wintering grounds  (Wu et al. , 2018) , and that variation in migration behavior between closely related subspecies can affect GM  (Turjeman et al. , 2020). However, our study is the first to benefit from an extensive comparative dataset (comprising 52 species breeding in the temperate zone) that allows us to search for conserved GM patterns associated with long-distance migration that discriminate migrating and non-migrating species at their breeding grounds.