dataesr/scientific-paragraphs-categorization huggingface dataset:
0; IBM Corp, Armonk, NY, USA) for exploratory data analysis, and SAS (version 9. 4; SAS Institute Inc. , Cary, NC, USA) for big-data analysis. A two-sided p value of less than 0. 05 was considered statistically significant. We performed the negative binomial regression model (endpoint, incidence rate of any hospital admission) with IRRs and 95% confidence intervals (CIs)  [38] [39] [40]. Models were adjusted for infant sex, calendar period of birth (2009-2010, 2011-2012, and 2013-2015), birth season (spring, summer, autumn, or winter), region of residence (rural or urban)  32, 33, 41  , household income (high, middle, or low), preterm birth (≤36 weeks), and low birth weight (≤2499 g).
Acknowledgements This work was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korea government  ( 2021R1I1A2059735  [S. W. L. ] and  RS-2023-00248157  [D. K. Y. ]) and a grant ( 21153MFDS607  [J. Y. S. ]) from  Ministry of Food and Drug Safety of South Korea  in 2021-2025. The funding agencies had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.
Data availability The datasets analysed during the current study are available in the National Health Insurance Service, South Korea, https://nhiss. nhis. or. kr/bd/ab/bdaba000eng. do. This protects the confidentiality of the data and ensures that Information Governance is robust. Applications to access health data in South Korea are submitted to the National Health Insurance Service, South Korea. Information can be found at https://nhiss. nhis. or. kr/bd/ab/bdaba000eng. do. Source data (Fig. 3 ) are provided with this paper. Source data are provided with this paper.
Code availability This study did not generate new or customized code/algorithm. Statistical analyses were performed using R software (version 3. 1. 1; R Foundation, Vienna, Austria) for generating figures, Statistical Package for Social Sciences (version 25. 0; IBM Corp, Armonk, NY, USA) for exploratory data analysis, and SAS (version 9. 4; SAS Institute Inc. , Cary, NC, USA) for big-data analysis. The codes used in the analysis are available from the corresponding author.
levels of physical activity and mental health status, respectively. The Emotional Eater Questionnaire (EEQ) was used to assess food consumption related to emotion. Pearson and Spearman correlation analysis test were used in data analysis. Results. -Levels of depression and stress were mild and moderate, while levels of anxiety were severe and very severe in most elite athletes. There were levels of low emotional eating by elite athletes during the COVID-19 pandemic. Physical activity levels were negatively correlated with psychological mood measures (P ≤ 0. 05), while there were positive correlations between emotional eating behaviours and psychological mood measures (moderate correlation; P ≤ 0. 01) and light physical activity levels (weak correlation; P ≤ 0. 05). Conclusion. -This study provides the first preliminary evidence showing that the COVID-19 lockdown conditions negatively influenced the eating habits and levels of physical activity and mental health in elite athletes.
Participants The Cochran formula was used to determine the sample size for this study  [15]. We recruited 383 elite athletes to participate in the study based on cluster random sampling, using the following eligibility criteria: if they had competed in national and international athletic competitions, either individual event or in a team event. We distributed 567 questionnaires in the northern, southern, western, eastern and central parts of Iran. The inclusion criteria were used to recruit 383 elite athletes. A flowchart of the study, including study subject recruitment and data collection, is depicted in Fig. 1. a From Abdel Wahed and Khamis Hassan (2017)  [24].
❚ METHODS We performed a descriptive cross-sectional study using data collected from August to December of 2018. The population of interest was classified into two different groups: PDs and neurology trainees. Program Directors were defined as faculty with curricular oversight in the accredited neurology programs of Brazil. The neurology trainees included neurology residents and recently certified neurologists (RCNs), defined as first-time board-certified neurologists between 2015 and 2017. In Brazil, neurology residency consists of three years of training: the first year in internal medicine (postgraduate year 1, PGY-1) and two additional years in neurology (PGY-2 and PGY-3). As such, only PGY-2 and PGY-3 residents were recruited for the study, as they were in the neurology-specific stage of their training.
Between 2015 and 2017, an average of 185 graduating neurologists were trained each year, totaling an estimated 555 possible RCNs in the study period and a possible total of 998 individuals who could constitute the trainee group. Through the ABN e-mailing list, we attempted to recruit all eligible physicians at all 27 of Brazil's federative units (26 States and 1 Federal District) and all 97 registered neurology programs. The Instituto Brasileiro de Geografia e Estatística divides the country into five regions (North, Northeast, Central-West, Southeast, and South), based on natural, social, cultural, and economic similarities. (23) uestionnaires were sent to all regions, and results were organized based on these five regions. The dataset used in this study can be found in the Harvard Dataverse repository.
Similarly, responses from PDs were obtained from 38 different institutions, which corresponds to 39% of the country's residency programs. The state of São Paulo represented the largest number of participants, with 54. 4% (n=55) of neurology trainees and 36. 5% (n=23) of PDs, followed by the state of Minas Gerais, with 10% (n=11) of neurology trainees and 17. 4% (n=11) of PDs. The other participating states included Rio Grande do Sul, Bahia, Ceará, Distrito Federal, Paraná, Rio de Janeiro, Goiás, Santa Catarina, Amazonas, Mato Grosso do Sul, Pernambuco, Rio Grande do Norte, Sergipe, Alagoas, and Pará. We compiled the institutional results by region, demonstrating the distribution and representation of our data for each group (Figure  1 ).
First, recall bias may have resulted from the self-reporting nature of the study and the absence of a formal curricular assessment of the institutions. For example, some respondents may not have remembered attending classes or receiving feedback. However, the fact that residency PDs overwhelmingly acknowledged the absence of formal BBN training confirms the relevance of the problem. Second, communication techniques may have been taught in medical school rather than during residency. Third, although almost 50% of neurology PDs participated in this study, only 10% of residents and RCNs answered the questionnaire, which may have introduced a level of selection bias, limiting the generalizability of our findings. Lastly, our data were collected in late 2018, before the Brazilian Ministry of Education published BBN as part of its recommended set of core competencies for neurology residents in 2019.
Data collection. Using a self-administered questionnaire, DC patients were asked to provide socio-demographic information including marital and occupational status. Additional information regarding socio-demographic status (age, sex) and medical information (cancer type and prognosis, date of cancer diagnosis, date of first treatment, inclusion in a clinical trial) were collected from the medical charts. The prognosis was measured by the metastatic status of the cancer. On-going medical treatments and comorbidities were collected from the patient's medical history. Then DC patients completed five self-report questionnaires. The five questionnaires used were chosen because they are frequently used in the literature and they were validated in their French version beforehand. Preferences for involvement in medical decision-making was assessed using the Control Preference Scale (CPS)  29, 30.
Results From January to March 2022, 135 DC patients were included. Of these, 115 (85. 2%) returned questionnaires. The sociodemographic and disease-related characteristics of DC patients are introduced in Table  1. No data were missing. The average age of DC patients in the population was 64. 7 years (σ = 10. 9). Seventy-six (66. 1%) were male. Seventy-six (66. 1%) patients were retired, 12 (10. 4%) were unemployed and 27 (23. 5%) were employed. More than 80% of the patients had at least one comorbidity that required chronic treatment in addition to cancer treatment. Fifty-two (45. 2%) patients had colorectal cancer, 29 (25. 2%) had hepatocellular carcinoma and 22 (19. 1%) had pancreatic cancer. A majority of patients had metastatic cancer (63. 5%) and a minority were Regarding the CPS, nine patients (7.
Acknowledgments: The authors thank the " Centre Technologique des Microstructures", CTµ (Université Lyon 1) , and the  "Centre d'Imagerie Quantitative Lyon-Est", CIQLE  ( Université Lyon 1 ), for providing access to SEM and confocal microscopies. Peptide synthesis was performed using Synbio3 platform supported by  GIS IBISA and ITMO Cancer (Montpellier, France).
Several recent examples of applications hold promise to serve as prognostic biomarkers for CKD progression. The urinary excretion of epidermal growth factor (uEGF) was identified as a biomarker of CKD progression using a kidney biopsy transcriptome-driven approach. 63 This biomarker was further identified as an independent predictor of kidney function decline in subsequent observational prospective cohort studes. 64, 65 Molecular profiling has identified a patient subgroup within nephrotic syndrome with poor outcome as well as kidney tumor necrosis factor (TNF) pathway activation  66  identified by urine biomarkers, monocyte chemoattractant protein-1 (MCP1), and tissue inhibitor matrix metalloproteinase 1 (TIMP1). 66 Based on these findings, clinical trials utilizing noninvasive biomarkers of pathway activation to target therapies, improve response rates, and facilitate personalized treatment in nephrotic syndrome have been initiated (ClinicalTrials.
The following information was collected: gestational age, gender, age, type of feed (breast milk or infant formula), and complementary feeding. Descriptive statistics were summarized with mean and standard deviation for normally distributed continuous variables, median and IQR for non-normally distributed variables, and differences in CoMiSS values were analyzed with appropriate tests. Data from 609 infants were obtained. The overall median (Q1-Q3) CoMiSS values were 3 (1-5). Significant differences were found across age groups (p < 0. 001), but not across groups based on gender (p = 0. 551) or feeding type (p = 0. 880). Conclusions: This study provided CoMiSS values in presumed healthy infants aged 6-12 months. Additional studies should be conducted to establish the use of CoMiSS to assess cow's milk-related symptoms in infants 6 months and older.
Sample size {14} Based on a large database that included 35 ICUs  [3] , the mortality rate associated with severe acute exacerbations of COPD is 17% and the mean duration of ventilation (invasive or NIV) is 5. 8 days for alive patients (median 4 days). According to this database and previous published studies, 75% of patients are treated with NIV, while 25% are treated with invasive mechanical ventilation. We performed several simulations in which we varied the mortality rate (binomial, 15-19%) and the mean duration of ventilation (log-normal distribution, 5-7 days) and calculated the required sample size according to alpha risk = 5% and beta risk = 20% and various hypotheses of efficacy (none or 1% reduction in the mortality rate, combined with a 2-4-day reduction in days of ventilation) and a Wilcoxon test of the resulting VFD in the two groups.
Data management {19} Designated staff will enter the data into the eCRF under the supervision of the principal investigator in each centre, who will be responsible for ensuring that the data collected are complete, accurate, and that entry is performed in a timely manner. A data manager will be in charge of checking missing or inconsistent data, and providing queries to be solved. In case of missing data, the reason will be noted. Corrections, with the reason for the corrections, will also be recorded/tracked in the eCRF. After resolution of all queries, the database will be locked for statistical analysis.
Confidentiality {27} Participants included in this research will be identified using a unique anonymous reference number that will be used for the entire duration of the study. For eligible patients who are not included in the study, the date and reason for non-inclusion will be anonymously collected in order to build a patient flow chart. All study-related information will be stored securely at the study site. All participant information will be stored in locked filing cabinets in areas with limited access. All reports, data collection, processes, and administrative forms will be identified only by a coded identification number to maintain participant confidentiality. All records that contain names or other personal identifiers, such as locator forms and informed consent forms, will be stored separately from study records identified by code number.
Statistical methods for primary and secondary outcomes {20a} All analyses will be performed by the study statistician according to a predefined statistical analysis plan, using the latest versions of R software© (R Foundation for Statistical Computing, Vienna, Austria) or Statistical Analysis System© (SAS). A two-tailed p value of less than 0. 05 will be considered as indicating statistical significance. In accordance with the CONsolidated Standards Of Reporting Trials (CONSORT) statement, a flow diagram will describe the progress of patients in the two groups through the phases of the trial (enrolment, intervention allocation, intervention received, follow-up and data analysis). The analysis will be performed on an intentionto-treat basis. In case of premature stop or withdrawal from the study, patients will not be substituted.
Missing values will be described and, according to nature and frequency, multiple imputation methods will be used. A per-protocol analysis will be used as a sensitivity analysis, excluding patients who were wrongly randomized or did not receive the allocated intervention. Comparative analysis will systematically be done with (main analysis) and without adjustment on randomization stratification factors. Quantitative data will be described as means, medians, standard deviations, and interquartile ranges. Nominal data will be described as percentages with 95% confidence intervals. Proportions of missing data will be given. Patient characteristics will be described by centre and compared. Quantitative data will be compared using t-tests or non-parametric tests (Mann-Whitney sum rank) when the distribution does not approach symmetry even after suitable transformation (Tukey's ladder of powers).
Qualitative data will be compared by means of chi-square tests or Fisher exact tests. Time-to-event data (survival) will be described using Kaplan-Meier curves. The analysis of the primary outcome (i. e. , distribution of VFDs that is highly skewed) will be performed by a multivariate non-parametric test, taking into account stratification and minimization criteria used in the randomization  [21]. The final test will be performed at a 0. 049 two-sided alpha risk to guarantee a global 5% alpha level. The main analysis will be performed according to the intention-to-treat principle on the full dataset, while analyses on imputed data dealing with missingness will be considered as sensitivity analyses. As a secondary analysis, a time-to-event model, where death and becoming ventilator free are competing risks, with a fixed factor corresponding to the randomization arm, centre being considered as a random factor, taking into account other prognostic factors and their interactions with the randomized treatment, will be developed.
Patient and public involvement {31c} Patients and the public were not involved in the study design. Participants will be able to have access to the findings of the study on reasonable request. Data are available on reasonable request. The procedures carried out with the French data privacy authority (Commission Nationale de l'Informatique et des Libertés) do not provide for the transmission of the database, nor do the information and consent documents signed by the patients. Consultation by the editorial board or interested researchers of individual participant data that underlie the results reported in the article after deidentification may nevertheless be considered, subject to prior determination of the terms and conditions of such consultation and in respect for compliance with the applicable regulations.
Funding {4} The study was funded by grant from the  French Ministry of Health  obtained in 2018 ( Programme Hospitalier de Recherche Clinique ): PHRC-18-0127 study design, protocol preparation, or future data analysis and interpretation. AF reports honoraria by  Fisher & Paykel  for a lecture during  SFMU  Congress  2022AMD  reports grants from  Fischer Paykel , and personal fees from  Air Liquide and Addmedica , all outside the submitted work. TS reports personal fees from ADEP assistance,  AstraZeneca France ,  Boehringer Ingelheim France ,  Chiesi France ,  Lungpacer Inc , past 3 years), all outside the submitted work. PA and SL declare that they have no competing interests. AD reports grants from the  French Ministry of Health , Assistance publique -.
Availability of data and materials {29} The investigators will make the documents and individual data strictly required for monitoring, quality control, and audit of the study available to dedicated persons, in accordance with laws and regulations in force (Articles L. 1121-3 and R. 5121-13 of the Code de Santé Publique -CSP, French Public Health Code). The datasets used and/or analysed during the current study will be available from the coordinating investigator on reasonable request. The procedures carried out under the French data privacy authority (Commission Nationale de l'Informatique et des Libertés) do not permit the transmission of the database, nor do the informed consent documents signed by the patients. Consultation by the editorial board or interested researchers of individual participant data that underlie the results reported in the article after deidentification.
Data sources Data were collected between September 14th 2020 and October 27th 2020 via an online questionnaire distributed exclusively to the directors of the NHs and covering the period from March to July 2020. We followed the recommendations for developing high-quality online questionnaires  [15]. All these informed data were completed and validated using administrative data, routinely collected, available for each NH of the network over the study period. The cumulative number of deaths per NH was also cross-checked with the number of deaths reported to the public health authorities according to a standardized definition  [16]. In order to characterize the magnitude of the epidemic in each NH area, we used excess all ages mortality data during the first wave produced by INSEE  [17]. Thus, three levels were considered: low (category of reference for analysis), moderate and high, corresponding to counties with a mean excess of death of 5.
2%, 44. 5% and 110. 5% respectively. In order to illustrate the magnitude of the epidemic according to the French metropolitan regions, we used data on mortality in the elderly population (≥ 80 y. o. ) (as number of COVID-19 death recorded over the elderly population) during the first wave from the French National Mortality Database (INSERM CepiDC) provided by Santé publique France, the French public health agency.
We calculated odds-ratio (OR) and adjusted oddsratio (aOR) from the multinomial logistic regression model to estimate occurrences for all NHs. Factors associated with an episode of concern with a p-value ≤ 20% or considered epidemiologically relevant (as well as mean age and dependency score) and not correlated with each other, were included in the multivariable multinomial logistic regression model. Backward stepwise regression using AIC (Akaike criterion information) was used to elaborate the final model. All analysis were performed on available data. Analyses were conducted using the free software R, version 3. 6. 2. The database, which does not contain any individual data (only data concerning nursing homes and aggregated data on residents such as average age, dependency level, and number of deaths), has been declared to the CNIL, the French national commission for information technology and civil liberties, under the registration number N°2218181.
Data collection and de nitions of clinical events For each patient clinical data referred to pregnancy (gestational diabetes, preeclampsia, antenatal steroids), birth (gestational age at birth, birth weight, gender, Apgar score at 5 minutes, pH, lactates and haemoglobin from cord blood or venous blood in the rst hour of life) and clinical status within the rst week in NICU were prospectively recorded into the medical chart. Infants were classi ed as small for gestational age (SGA) if they were born with a birth weight < 10th percentile on customized AUDIPOG curves for male and female neonates  [16]. CTH was started in infants with HIE according to international guidelines  [15, 17]. Infants aged ≤ 6 hours with neonatal HIE who met the 2 following criteria: (1) a severe perinatal asphyxia (de ned by a metabolic acidosis (pH ≤ 7.
A second model was performed to consider the factors linked to care procedures only, including UVC position, UVC duration, CTH, and inotropes. All the statistical analyses were performed using the statistical software SAS 9. 4, ©2002-2012 SAS Institute Inc. , Cary, NC, USA, for Windows version 9. 4. Graphs were performed with GraphPad Prism 5. 0 (GraphPad Software, San Diego, CA). Statistical analyses were carried out with a two-sided alpha level of 0. 05.
Study Population and Outcome This observational monocentric prospective study, including in-chronic and out-chronic HD patients, adheres to the Standard for reporting diagnostic accuracy studies  8  and follows the guidelines outlined in the Declaration of Helsinki and the Declaration of Istanbul, with a favorable opinion from the Research Ethics Committee. Participants aged more than 18 years (n 5 103) were enrolled if they received HD for at least 3 months. As per the routine monthly nutritional assessment, blood was collected at the beginning of the middle week dialysis session. Demographic, clinical, and biological data were collected in dedicated health records DxCare (Table  1 ). As per French Haute Autorit e de Sant e guidelines, 2 the malnutrition status was defined as a body mass index (BMI) , 23 kg/m 2 (calculated using a dry weight at the end of the dialysis session) associated with hypoalbuminemia (defined as , 3.
A wide range of disciplines are directing their methods and tools to help address the challenges of healthcare. Chief among these are design and operational research (OR). Though they have much in common, these two disciplines have existed in isolation for most of their history and there is currently a gulf between the two research communities. In this position paper, we rapidly review the contributions of design and OR in healthcare. We then identify similarities and complementarities between the two disciplines and communities when they consider healthcare systems. Finally, we propose practical steps to enable better collaboration. Our focus is on finding ways in which the two disciplines complement each other. When applying design to healthcare services, designers may wish to learn from OR, which has a long history of supporting improvements in healthcare organisation and services, particularly using quantitative data and analysis and modelling methods.