dataesr/scientific-paragraphs-categorization huggingface dataset:
com/openai/baselines/tree/master/baselines/ppo2), for which a custom OpenAI environment has been designed with the Gym library  86. We have updated and connected the original code with our CFD library for simple reading and writing of the results (the code is shared publicly using the following link : https://github. com/jviquerat/pbo). The convergence properties are illustrated in Fig. 12  for a minimization test problem of two-and five-dimensional Rosenbrock functions, whose global minimum is notoriously difficult to catch for optimization algorithms and two-dimensional Branin function, that has two identical global minima. For this case, the single-step PPO-1 algorithm is benchmarked against classical ( µ-)-ES and CMA-ES evolutionary methods, all implemented in in-house production codes.
Data collection, cleaning, and analyses The USDA Ruminant Health Center and the National Park Service provided data for analyses. Data variables included signalment (year of birth, sex), management data (cohort name, date of entry into each quarantine phase, release date, identification including radio-frequency identification [RFID] tags, bangle tags, and study ID), and laboratory data (laboratory result with date and test type). R statistical software (version 4. 0. 3) was used to analyze data and for quality control. Management and brucellosis screening data during 2005 through 2021 were summarized and evaluated. Quarantine program data from the brucellosis quarantine feasibility study during 2006 through 2013,  13  were combined with quarantine program data during 2016 through 2021 for analyses.
A hazard ratio calculation using the latent-class time-to-event model also revealed no significant difference in model risk between male and female bison (95% CI, 0. 69 to 1. 89); the data were combined for all further analyses. A combined dataset of days until seroreactors were detected and the time-to-event model for all seroreactor bison are illustrated (Figure  3 ). Model evaluation revealed high confidence that the model fit well to the observed data (Supplemental Figure  S1 ).
Acknowledgments Research was funded by the  National Park Service and USDA. We would like to thank all collaborators for their assis-tance and support with this project, including  USDA Veterinary Services  staff,  National Park Service  staff,  Montana Department of Livestock  staff,  Robert Magnan  and the  Fort Peck Assiniboine and Sioux Tribes , and the  InterTribal Buffalo Council. Disclaimer: The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or US Government determination or policy.
Statistical analysis Maternal, pregnancy, labor, delivery, and neonatal characteristics were compared using Chi  [2]  or Fisher exact tests for categorical variables and Student's or Wilcoxon rank sum tests for quantitative variables, as appropriate. All tests were two-sided with p-values < 0. 05 defined as statistically significant. Multiple component analysis (MCA) was run on the 11 vulnerabilities aforementioned  [19]. MCA allows to reduce the dimensions in interpreting the variability explained in a dataset by combining variables that are associated together into factorial axis (linear weighted combination of the variables included in the analysis)  [11, 20]. The factorials axis being orthogonal it allows to avoid multicollinearity in regression analysis. The analysis of the MCA graphs did not detect any outlier within the population.
This analysis resulted in five clusters that represented five distinct social vulnerability profiles. The independent association between inadequate PCU, premature birth (< 37 GW), SGA status, and the social vulnerability profiles were tested using multiple logistic regression. Because stillbirth, late miscarriage and medical abortion were rare events within the profiles, the incidence rate ratio (IRR) were calculated using multiple Poisson regression. Adjustment was performed on maternal age, maternal origin, parity, BMI, high medical risk level before pregnancy and high obstetrical risk level before pregnancy. No multicollinearity was detected using variance inflation factor. Visual inspection of residual plots did not reveal any obvious deviations from homoscedasticity or normality. Factominer package  [20]  was used to perform MCA and HCPC analyses.
Introduction After closure of patent foramen ovale (PFO) due to stroke, atrial fibrillation (AF) occurs in up to one in five patients. However, data are sparse regarding the possible pre-existence of AF in these patients prior to PFO closure, and about recurrence of AF in the long term after the procedure. No prospective study to date has investigated these topics in patients with implanted cardiac monitor (ICM). The PFO-AF study (registered with ClinicalTrials. gov under the number NCT04926142) will investigate the incidence of AF occurring within 2 months after percutaneous closure of PFO in patients with prior stroke. AF will be identified using systematic ICM. Secondary objectives are to assess incidence and burden of AF in the 2 months prior to, and up to 2 years after PFO closure. Methods and analysis Prospective, multicentre, observational study including 250 patients with an indication for PFO closure after stroke, as decided by interdisciplinary meetings with cardiologists and neurologists.
Such data are essential for decision-making about the treatment of AF, particularly the need for anticoagulant therapy. In the absence of recurrent AF, discontinuation of anticoagulant therapy can be considered, whereas recommendations advise that it must not be discontinued if AF persists in these patients with prior stroke. 18  The objective of the PFO-AF study ('Incidence of Atrial Fibrillation in cryptogenic stroke with Patent Foramen Ovale closure') is therefore to assess the incidence of AF, using ICM, up to 2 months after percutaneous PFO closure due to ischaemic stroke. Secondary objectives are : ► To evaluate the incidence of AF established based on data collected by ICM in the 2 months prior to, and up to 2 years after percutaneous PFO closure. ► To identify the predictors of AF after PFO closure.
Implantable cardiac monitors (ICM) The ICM device will be implanted at month (M) 0 (figure  1 ), in order to have a 2 month 'control' period of observation prior to the procedure. Implantation of ICM devices is done during a consultation or in the outpatient unit of the Cardiology department, under local anaesthetic. All ICM devices used in the study will be the same, to minimise detection bias for AF between centres. The device used will be the Reveal Linq (Medtronic). Device parameters will be standardised, as described in the online supplemental material. Clinical and biological data as well as the patient's medical history will be recorded on the day of device implantation.
PFO closure procedure Percutaneous PFO closure will be scheduled to take place at M2 (ie, 2 months after ICM implantation) (figure  1 ), and within 6 months after the qualifying stroke. An ECG and reading of the ICM device will be performed in the 24 hours prior to PFO closure. The PFO closure procedure will be performed by the femoral venous approach under local anaesthesia with sedation, or under general anaesthesia. Procedures performed under local anaesthesia will be guided by TTE or TEE microprobe. The closure device implanted, and its size, are at the discretion of the operator. Procedural and echocardiographic data will be recorded, namely left ventricular ejection fraction; presence or absence of left ventricular hypertrophy, defined as a maximal septal thickness>15 mm in diastole; measure of the left atrium in the parasternal long axis view; measure of the left atrial area in the apical four chamber view; measure of left atrial strain.
Data coordination All data management and analysis will be centrally performed at the Cardiology Department at the coordinating centre (University Hospital of Besancon, France), where a dedicated team of data managers will be responsible for data collection and monitoring. Computerised checks will be performed to verify the coherence of the data, and queries will be generated in case of inconsistencies. A formal data monitoring process will be overseen by the Clinical Research Management Department (Délégation à la Recherche Clinique et à l'Innovation) of the coordinating centre (University Hospital, Besancon, France), who will be responsible for sending independent monitors to each site regularly to monitor files and check data entry.
Risk factors for the primary endpoint will be assessed using a logistic regression model. Univariate analysis (p<0. 10) will first be performed to select potential explanatory variables, which will subsequently be tested in a multivariate model (stepwise method with entry and retention significance levels of 0. 10 and 0. 05, respectively) and presented as adjusted ORs with 95% CIs. All analyses will be performed using SAS V. 9. 4 (SAS Institute, Cary, NC).
Results: A total of 1,079,406 pediatric EMS encounters were present in the dataset, of which 102,014 (9. 5%) had behavioral health provider impressions. Just over one-half of BHEs occurred in females (56. 2%), and 68. 1% occurred in patients aged 14-17 years. Telecommunicators managing the 9-1-1 calls for these events reported non-BHE patient complaints in 34. 7%. Patients were transported by EMS 68. 9% of the time, while treatment and/or transport by EMS was refused in 12. 5%. Prehospital clinicians administered sedation medications in 1. 9% of encounters and applied physical restraints in 1. 7%. Naloxone was administered for overdose rescue in 1. 5% of encounters.
Data Source ImageTrend software solutions (ImageTrend; Lakeville, Minnesota USA) are commonly used by emergency responders across the United States for the electronic capture and reporting of operational and clinical data related to emergency response incidents. This evaluation utilized the Collaborate database maintained by ImageTrend, which is a de-identified, prehospital electronic health record dataset that currently comprises approximately 44 million EMS activations from 2,502 EMS agencies providing emergency medical response in 50 United States' states and territories. The dataset is continually updated with new events forming a real-time data repository for industryrelevant surveillance, reporting, and research. The EMS agencies have the option to decline having their data become part of Collaborate.
Measurements Patient demographics and clinical variables used in this analysis reflect EMS clinician documentation extracted from the ePCRs, thus leveraging NEMSIS data definitions. Patient demographics included sex and age. Several response characteristics were also analyzed, including location of patient encounter, the complaint recorded by the 9-1-1 telecommunicator, provider impressions indicated by the EMS clinician, patient disposition, and EMS interventions. Age is recorded as a continuous variable by prehospital clinicians, however, for purposes of analysis, age was evaluated using the following categories corresponding to United States education levels and reflecting previous definitions proposed by the National Institute of Child Health and Human Development (Rockville, Maryland USA): 15 0-4 years (early childhood), 5-10 years (elementary), 11-13 years (middle school), and 14-17 years (high school).
5% of pediatric patients seen by EMS for BHEs refuse treatment and/or transport. The uniqueness of EMS usage patterns associated with the coronavirus disease 2019 (COVID-19) pandemic prompted examination of data by calendar year. The general profile of pediatric EMS encounters involving BHEs in terms of the distribution of demographic, clinical, and incident characteristics appeared relatively stable over the study  timeframe, with the exception of a noticeable decline in the proportion of BHE responses that occurred in school settings in 2020 that coincides with the period of wide-spread COVID-19 pandemic-related remote learning. 17  Two previous studies have used national datasets to explore the frequency of EMS encounters involving BHEs in the United States. 7, 8  An analysis of 2018 NEMSIS Version 3 data found that 7.
7, 18  Data from this study suggest that 69. 8% of EMS activations for pediatric BHEs resulted in transport. This estimate is similar to the overall pediatric transport rate of 69. 9% observed in 2019 NEMSIS data,  12  but is lower than the 88. 4% transport rate observed among pediatric EMS activations with a behavioral/ psychiatric chief complaint in that dataset. In the recent Australian study by Bourke, et al,  19  82% of children with mental health presentations were ultimately transported by EMS. The substantial transport rate in conjunction with the low frequency of interventions in this particular patient subgroup lends itself to exploration of protocols that allow for transport to non-ED/ alternative destinations, a model that has been tested with some success in Alameda County, California (USA).
30  Limitations This analysis was restricted to 9-1-1 responses and thus did not include patient encounters that occurred during interfacility transfers. The focus of this study was emergent situations, but prehospital clinicians often care for patients with behavioral health conditions when transporting them between medical or other care facilities. Information on patient race is not collected by all EMS agencies in the United States and was missing for 26% of records in this dataset. This precluded useful analysis of the occurrence of pediatric BHEs by race. The geographic representativeness of these data may be limited in that end users of this particular ePCR system are not necessarily evenly distributed across the country. While the Collaborate dataset represents a large (11 million activations annually) and broad (2500þ EMS agencies) sample of EMS activity across the country, these findings can only be considered representative of patients cared for by EMS agencies in the United States that utilize this particular software platform, and sampling error could not be determined due to database inclusion.
Materials And Methods This study was approved by the institutional ethics committee of our institution (#PADS20-260). According to French Law, no patient or parent objected to the use of the data for this research. All charts of patients under the age of 18 years presenting with PPA and RPA over the period 2010-2020 in the Pediatric Otorhinolaryngology Department of a tertiary-care University Hospital and the Otorhinolaryngology Department of another regional public hospital, both following the same management protocol, have been included. Patients presenting with other neck abscesses, such as uncomplicated peritonsillar abscesses and lateral cervical abscesses were excluded. Demographic data, medical history, pre-hospitalization course and treatments, blood test pro le, imaging, clinical signs, suspected etiology, whether referred by a physician, transferred from another center, consultation time, surgical status, medical treatment, and the presence of complications such as transfer to intensive care, intubation or tracheotomy, cervical traction for residual torticollis were collected.
Aim: To identify distinct HbA1c trajectories in people with type 2 diabetes (T2D) starting second-line glucose-lowering therapy. Materials and Methods: DISCOVER was a 3-year observational study of individuals with T2D beginning second-line glucose-lowering therapy. Data were collected at initiation of second-line treatment (baseline) and at 6, 12, 24 and 36 months. Latent class growth modelling was used to identify groups with distinct HbA1c trajectories. Results: After exclusions, 9295 participants were assessed. Four distinct HbA1c trajectories were identified. Mean HbA1c levels decreased between baseline and 6 months in all groups; 72. 4% of participants showed stable good levels of glycaemic control over the remainder of follow-up, 18. 0% showed stable moderate levels of glycaemic control and 2.
8 Such heterogeneity may potentially be attributable to differences in study populations and treatment regimens. Currently, no study has exclusively investigated HbA1c trajectories in people with T2D after initiation of second-line glucose-lowering therapy. DISCOVER was a global observational study of more than 15 000 people with T2D starting second-line glucose-lowering therapy. 11 Participants were recruited from 2014 to 2016 from 38 countries across six regions (Africa, the Americas, South-East Asia, the Eastern Mediterranean, Europe and the Western Pacific) and were followed up for 3 years. 11 Using data from DISCOVER, we aim to identify HbA1c trajectory groups in individuals at an early stage of T2D progression. This is the first study on HbA1c trajectories in people with T2D starting second-line therapy, and the first study on HbA1c trajectories on a global level, including low-and middle-income countries rarely or never having been previously studied.
11, 12 We also assess demographic, clinical and treatment characteristics associated with different trajectories. NCT02322762] in 37 countries and J-DISCOVER [NCT02226822] in Japan). 11, 12 Study methods have been described in detail previously. 11, 12 Countries were grouped into regions according to World Health Organization categories 13 (Data S1, Table  S1 ), and were categorized by gross national income using 2016 data from the World Bank. 14 rticipants were enrolled in DISCOVER from December 2014 to June 2016, and in J-DISCOVER from September 2014 to December 2015. To ensure that data were as reflective of routine clinical practice as possible, inclusion and exclusion criteria were kept to a minimum. Individuals with T2D were eligible for inclusion if they were starting second-line glucose-lowering therapy, provided that their first-line therapy was not an injectable agent or an herbal remedy/ natural medicine alone.
Second-line therapy was defined as the addition of one or more drugs, or a switch of drug to another class of hyperglycaemic drug, if first-line therapy was an oral form of monotherapy. Second-line therapy was defined as the discontinuation of a drug, a switch of at least one drug to another class of drug, or a third drug added, if first-line therapy was dual therapy. Similarly, so for first-line therapy being triple or quadruple drug therapy. Full inclusion and exclusion criteria are shown in Data S1, Table  S2. Eligible individuals were invited to participate in the study by their physician and provided written informed consent. A list of participating investigators can be found in Data S2. The study protocol was approved by the clinical research ethics committee in each participating country, along with the appropriate institutional review board at individual study sites.
| Data collection Data were collected at baseline (the date of second-line therapy initiation) and during follow-up at 6, 12, 24 and 36 months using a standardized electronic case report form, and were transferred to a central database via a web-based data capture system. Data collected at baseline and during follow-up included participant demographics (such as sex, age, body mass index [BMI] and duration of diabetes), clinical variables (such as HbA1c levels) and first-and second-line glucose-lowering therapies. In line with the observational nature of the study, variables were measured by participating physicians in accordance with routine clinical practice and data collection for these variables was not mandatory.
| Statistical analysis Participants in the DISCOVER study were included in the present HbA1c trajectory analysis if they had at least two HbA1c measurements during follow-up in addition to a baseline measurement. Data from China were excluded because of changes in regulatory requirements during the study (n = 1292). Data are presented as numbers (percentages), means (standard deviations [SDs]) and medians (interquartile ranges [IQRs]), as appropriate. Latent class growth modelling was used to model HbA1c patterns and identify groups of participants with similar HbA1c trajectories over time (baseline to 36 months). 6 The maximum likelihood method was used to estimate model parameters, with polynomial functions fitted using quadratic and cubic orders. The Bayes Information Criterion (BIC) was used to help assess the optimal number of trajectory groups, with higher BIC values indicating a better model fit than lower values.
15, 16 In addition, each HbA1c trajectory group had to contain at least 2. 0% of our analytical cohort to avoid identification of groups containing only a small number of participants. 15, 16 Very small groups increase statistical uncertainty and numerically destabilize models. The clinical interpretation of each trajectory model was also considered. Logistic regression models were used to investigate the odds of participants being assigned to different HbA1c trajectory groups, with baseline covariates (sex, age, BMI, country income and second-line therapy [oral monotherapy, dual oral therapy, three or more oral therapies or an injectable agent]) as fixed effects. Our analyses were standard logistic models and hypothesis generating. Multiple imputation was used to impute any unreported data for the independent variables included in the logistic regression model.
Iterative sequential regression was employed to sample missing values from the predictive distribution of each variable, conditional on all other variables included in the model. Variables included in the imputation model were the dependent variable (HbA1c trajectory groups) and independent variables (patient demographics [such as sex, age, BMI and country income] and first-and second-line glucose-lowering treatments). Ten randomly imputed datasets were generated in this way. Analyses were repeated on each imputed dataset and model estimates were pooled across imputations using Rubin's rule. Statistical analyses were performed using the SAS 9. 4 statistical software system (SAS Institute Inc. , Cary, NC).
Given the observational nature of the study, there was no requirement to record all study variables, and a complete dataset was not available for all participants. Indeed, the entire DISCOVER cohort could not be included in this analysis as only those with three or more HbA1c level measurements, including a baseline measurement, were eligible. This criterion meant that a large proportion of participants was excluded from the analysis (n = 5396). This has the potential to introduce selection bias, as those participants with a greater number of HbA1c measurements may have been better monitored, better managed or may have had characteristics related to more favourable diabetes management. However, in order to adequately assess a trajectory, at least three HbA1c measurements are required. Additionally, the observational nature of this study prevents the control of regression to the mean; however, the trajectories of groups with similar baselines differed remarkably.
Patients were treated with nivolumab 240 mg and BV 1. 8 mg/kg once every 3 weeks until disease progression or unacceptable toxicity. Primary end point was objective response rate (ORR); secondary end points included complete response rate, duration of response, progression-free survival (PFS), and overall survival (OS). Safety was monitored throughout. At final database lock (30 March 2022), 29 patients had received nivolumab plus BV; median follow-up was 39. 6 months. Investigator-assessed ORR was 73. 3%; median time to response was 1. 3 months (range, 1. 1-4. 8). Median PFS was 26. 0 months; median OS was not reached. PFS and OS rates at 24 months were 55. 5% (95% confidence interval [CI], 32. 0-73. 8) and 75. 5% (95% CI, 55. 4-87. 5), respectively. The most frequently occurring grade 3/4 treatment-related adverse event was neutropenia.