dataesr/scientific-paragraphs-categorization huggingface dataset:
When local guidelines referred to another guideline (e. g. , from the ESC), we counted it as the second guideline. If GPs listed something other than a guideline, we classified it under "Others" (S1 Appendix). The complete survey described eight case-vignettes where oldest-old male or female patients presented for a routine control (Additional file 1 in  [14] ). These patients had no symptoms suggesting hypertension and took no antihypertensive medication. The vignettes differentiated by the following variables: systolic blood pressure (SBP; options: 140 or 160mmHg), presence or absence of history of cardiovascular disease like myocardial infarction or stroke, and presence or absence of frailty. In each case, GPs were asked to decide whether they would start antihypertensive treatment. In our study, we analysed data from four of the eight case-vignettes that applied to frail oldest-old patients.
Statistical analysis We used descriptive statistics to compare baseline characteristics in the whole sample and stratified by guideline use (rather yes and yes = yes; neutral, rather no and no = no). We used a Chi2-test to assess categorical data and a complete case analysis to handle missing data. To assess how GPs varied in their use of guidelines when they treated hypertension in the oldestold, we calculated the crude proportions and 95% confidence intervals (CI) per country. To assess the role that guidelines played in GPs' decisions, we used a mixed-effects Poisson model to calculate percentages and 95% CIs of GPs who decided to treat hypertension across the four case-vignettes. We adjusted the model for sex and years of experience and stratified it by guideline use. We used a mixed-effects model to account for a clustering effect within each country and used the same model to stratify the guidelines that GPs said they followed further.
Prof. Streit 's research was supported by grants ( P2BEP3_165353 ) from the  Swiss National Science Foundation (SNF)  and the  Gottfried and Julia Bangerter-Rhyner Foundation ,  Switzerland. Christian Mallen  is funded by the  NIHR Collaborations for Leadership in Applied Health Research and Care West Midlands , the  NIHR SPCR  and a  NIHR Research Professorship in General Practice , ( NIHR-RP-2014-04-026 ). The views expressed are those of the authors and not necessarily those of the  National Health Service , the  NIHR  or the  Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data collection The study focused on the dispensing phase, from the care unit order to the sealing of the dispensing boxes before they are sent to the wards. Particular attention was given to measuring the changes in processes and costs before and after the implementation of the robots. Hence, two categories of data were collected for this study: data describing the dispensing system of medicines and data related to the ADS implementation costs. The modelling of the dispensing process before and after implementation of the ADS was central to understanding and identifying organisational and financial impacts. We used process mapping method to conduct a two-hour group interview with representatives of the different professions involved in the distribution process: pharmacists, pharmacy assistants and healthcare executives from the pharmacy, as well as nurses, the storekeeper, the purchase order manager and the system information manager.
Data were collected on preparation time for wards allocation renewal, excluding preparation of urgent requests, which are of a more random nature. The analysis also distinguished dispensing time from time lost due to interruption (handling of other tasks, robot breakdown), as well as from the time needed for the final validation of the unit medication orders. The pharmacy staff conducted a study on the number of dispensing errors by care unit orders, before and after implementation of the ADS. In this study, the errors of dispensing were monitored every day during the week of the study, at three time periods: December 2016, February and June 2017. It involved comparing several wards' department orders with the actual content of the dispensing box prepared by the assistants. The measured values do not necessarily represent common practices because of the Hawthorne effect.
Contributions and outline The contribution is to propose a procedure to study correlations between equity markets with the objective of finding relevant stable eras of financial integration. The study is conducted over asset price time series like in most econometric literature (e. g. [25] ). The dataset used is detailed in Section 2): it consists in a collection of national equity market indices from (up to) 32 different countries for the years 1955 to 2015. The procedure consists of three steps that are sketched in Fig. 1  and detailed in Section 3. The first step constructs, from the empirical estimation of the covariance structure, a graph of relations between countries, based on a minimal spanning tree (MST) from the correlation distance; it gives a time-dependent graph thanks to doing this analysis on (long) sliding windows of several years (with overlaps).
Dataset. The dataset used comprises equity market price indices for up to 32 countries world-wide, compiled by the Monthly Monetary and Financial Statistics of the OECD, a reference institution for economic history data. These indices are representative at national levels, including the closing daily values of all listed shares or a broad coverage, normally expressed as simple arithmetic averages. Data are collected monthly, from Jan. 1955 to 2015 and available at the FRED database (<https://fred. stlouisfed. org/>). In the fist years, only 13 countries are documented, the maximum number of 32 being attained from 2005 on. We have consistently checked that, with the exception of the 1960s where there is a jump from 13 to 18 countries, there is no correlation between this increase and the evolutions reported hereafter.
Acknowledgment This work was conducted in the CAC (Cliometrics & Complexity group) at IXXI (Institut Rhônalpin des Systèmes Complexes), and supported by the GRAPHSIP project ANR-14-CE27-0001-02, and by the ACADEMICS Grant given by the IDEXLYON project of the Université de Lyon, as part of the "Programme Investissements d'Avenir" ANR-16-IDEX-0005.
Train dataset is from 01-Jan-2010 to 31-Dec-2017, while test data set ranges from 01-Jan-2018 to 31-Dec-2019. Results of the various trained networks can be visualized in http://www. aisquareconnect. com/deeprl/models. html. Electronic copy available at: https://ssrn. com/abstract=3871071 DRL algorithm: To find the optimal action π * (S t ) (the portfolio allocation) according to states S t (financial information) given a reward R t (the best net portfolio final performance), we represent the policy as our 3 entries network and use deep policy gradient method with non linear activation (Relu). We use buffer replay to memorize all marginal rewards, so that we can start batch gradient descent once we reached the final time step. We use traditional Adam optimization so that we have the benefit of adaptive gradient descent with root mean square propagation  [3].
Third is expedited review, done in cases when a project already has ethics approval from a TCPS 2-compliant Canadian university or hospital or if a project is using anonymized non-public secondary data. An example of this would be using anonymized data from a hospital. HOF studies involving human subjects where data is being collected to answer a research question would always require ethics approval. Studies involving patient recruitment for observation or evaluation such as a usability study would be classified as minimal risk review and typically have turnaround times of up to six weeks. Expedited review would take up to three weeks. However, while general guidelines for ethics approval are quite clear, the actual process of review can vary significantly depending on several factors. One factor is the affiliation of the researcher and where the study is being conducted.
The private group Luz Saúde, owner of the Hospital da Luz network where we operate, has a group-wide CES and a group-wide Clinical Research Commission (CIC), supporting its 30 hospitals and clinics. The CIC assesses the clinical relevance and scientific quality of submitted clinical studies. HOF studies are evaluated by the CIC whenever the objective of the study is within the definition of clinical research. Additionally, studies should always be evaluated by the CES, even if the study is not considered to be clinical research, unless patient data is not used. If patients are involved in a study, informed consent may also be required, and its clarity and completeness is evaluated by the CES. If personal data of participants based on the GDPR definition are also required, a Data Protection Impact Assessment (DPIA) should be performed.
In general, this means that a study of healthcare professional's use of IT, organizational studies and usability studies involving healthcare professionals do not require ethical approval according to the current Swedish legislation. If patients are involved, we however need to consider whether any health-related data would need to be gathered; if so -ethical approval would be required. This could be the case even if you only test an application with mock data and you document information about the study participant's current or past health issues, or their contact with healthcare. Therefore, in most health informatics research, ethical approval is sought whenever patients will be involved in the study, as sensitive personal data regarding their health will likely be documented. All ethical reviews are handled by the Swedish Ethical Review Authority 6 , which started on the first of January 2019.
The University also upholds the Federal Health Insurance and Portability and Accountability Act (HIPAA) for the protection of patient health information and the Oklahoma state laws pertaining to protections of vulnerable populations including emancipated minors and cognitively impaired individuals. Criteria used for defining and policing research are consistent with federally published standards and are similar between institutions (US Code of Federal Regulations 45FR. 46. 102; Bailey, Hastings Center Report, 2006). We submit all projects -including HOF work -to our institutional IRB well in advance of any data collection. Many IRBs, including at the University of Oklahoma, offer an expedited pathway for quality improvement efforts, educational programs, and non-experimental technologies. Typically, these studies do not include participant consent forms, different exposures, randomization, or a control group.
This article describes a new software (GIBBSTHUR) that provides Bayesian estimation of variance components and predictions of breeding values for ranking traits generated from equine competitions based on a Thurstonian approach. The GIBBSTHUR software was developed in FORTRAN 90 and can be executed in UNIX, OSX, or WINDOWS environments, and is freely available in a public repository (https://github. com/lvaronaunizar/Gibbsthur).
In horse breeding, that model has been tested on simulated data  [10]  and has been applied to trotter horses  [11]  and the results of endurance competitions  [12]. The practical use of the method has been limited, however, partly because of the absence of suitable and user-friendly software. The objective of this note is to present a new free software (GIBBSTHUR) that implements the Bayesian estimation of variance components and the prediction of breeding values for ranking traits based on a Thurstonian Model. In addition, the software can analyze continuous and threshold traits jointly with the underlying liability of the ranking traits.
The Software: Input and Output Files The software (GIBBSTHUR) was developed in FORTRAN90 and is based on the TM software  [14]. It is available at https://github. com/lvaronaunizar/Gibbsthur. Running the program requires a parameter file ('parameters. txt'), which must include the names of the pedigree and data file; the number of additional traits and their nature (continuous, threshold, or ranking); the description of the model of analysis for all the traits (systematic, random, and genetic effects); and the hyperparameters of the Gibbs sampler implementation that comprise the total number of iterations, the number of burn-in iterations to discard, and the thin interval. The pedigree file must include a list of the individual, sire, and dam entries numbered from 1 to the number of individuals.
They are computed with the output of the Gibbs sampler iterations after the burn-in. The file "Breeding. txt" includes the posterior mean and standard deviations of the breeding values for the ranking traits and the additional traits, and they can be directly used for selection purposes. The file "Solutions. txt" includes the posterior mean and standard deviations of the systematic and random environmental effects for the ranking and the additional traits. The file "Iterations. txt" includes the samples of each iteration of the Gibbs sampler for the variance and covariance components, which can be used to analyze in detail the marginal posterior distributions. A more detailed description is available at https://github. com/lvaronaunizar/Gibbsthur.
Simulation Example A simple pedigree comprising 100 sires and 500 dams generates 2000 horses that compete randomly in 2000 events of 10 horses each. On average, each horse competes in 10 events. The liability was generated by a model that included a general mean, one additive genetic effect, and a residual. The additive genetic and residual variances were set to 0. 25 and 1 (h 2 l = 0. 2), respectively. In addition, we simulated a correlated continuous trait that had additive and residual variances of 1 and 2 (h 2 l = 0. 33), respectively, and additive genetic and residual correlations with the liability of the ranking trait of 0. 3 and -0. 2, respectively. The model of analysis with the GIBBSTHUR software included two systematic effects (a general mean and an event effect), and the additive genetic and residual random effects.
Simulation Example A simple pedigree comprising 100 sires and 500 dams generates 2000 horses that compete randomly in 2000 events of 10 horses each. On average, each horse competes in 10 events. The liability was generated by a model that included a general mean, one additive genetic effect, and a residual. The additive genetic and residual variances were set to 0. 25 and 1 (ℎ = 0. 2), respectively. In addition, we simulated a correlated continuous trait that had additive and residual variances of 1 and 2 (ℎ = 0. 33), respectively, and additive genetic and residual correlations with the liability of the ranking trait of 0. 3 and -0. 2, respectively. The model of analysis with the GIBBSTHUR software included two systematic effects (a general mean and an event effect), and the additive genetic and residual random effects.
Results and Discussion The posterior distribution of the heritabilities and the genetic and residual correlations is presented in Figure  2. The simulated values were within the Highest Posterior Density (HPD) at 95% of the posterior distributions, which confirmed that the software can retrieve the simulated parameters for continuous and ranking traits. Slight differences between the mode of the posterior distributions and the simulated values can be expected, given the small size of the dataset. Figure  3  shows the predicted and simulated breeding values for the continuous and ranking traits. The correlation between the predicted and the simulated breeding values was higher for the continuous (0. 89) than it was for the liability of the ranking trait (0. 83), although, in both cases, the correlation was very strong because the amount of information available for each horse was large (on average, 10 phenotypic records).
The simulated values were within the Highest Posterior Density (HPD) at 95% of the posterior distributions, which confirmed that the software can retrieve the simulated parameters for continuous and ranking traits. Slight differences between the mode of the posterior distributions and the simulated values can be expected, given the small size of the dataset. Figure  3  shows the predicted and simulated breeding values for the continuous and ranking traits. The correlation between the predicted and the simulated breeding values was higher for the continuous (0. 89) than it was for the liability of the ranking trait (0. 83), although, in both cases, the correlation was very strong because the amount of information available for each horse was large (on average, 10 phenotypic records). The ability to discriminate between the performances of the individuals in competition events is illustrated in Table  1 , which provides a detailed description of the phenotypic information for the ranking traits of the best and worst predicted breeding values.
The best individual won 8 of 13 races, and the horse with the worst breeding values came last in 5 of 8 races. The simulated values were within the Highest Posterior Density (HPD) at 95% of the posterior distributions, which confirmed that the software can retrieve the simulated parameters for continuous and ranking traits. Slight differences between the mode of the posterior distributions and the simulated values can be expected, given the small size of the dataset. Figure  3  shows the predicted and simulated breeding values for the continuous and ranking traits. The correlation between the predicted and the simulated breeding values was higher for the continuous (0. 89) than it was for the liability of the ranking trait (0. 83), although, in both cases, the correlation was very strong because the amount of information available for each horse was large (on average, 10 phenotypic records).
In cases in which the structuring is high, Legarra and Ricard  [10]  proved with a simulation study that the results can be biased. Therefore, some other Animals 2020, 10, 1001 7 of 8 alternatives might be appropriate-e. g. , the approach suggested by these authors, who proposed a mixture model in which horses are classified into several groups based on their performance. Further research is needed to adapt the GIBBSTHUR software for use in those scenarios.
Processing The swing plane was computed based on the clubhead marker by computing the best fit plane with a root mean square criterion, during the last part of the downswing  (Morrison et al. 2014). Markers positions were used to animate a rigid bodies model  (Bourgain et al. 2018) , based on a multi-body kinematic optimization algorithm, on OpenSim software  (Delp et al. 2007). Resulting markers were used to compute two computational methods used to define the X-factor: The upper body markers were either the thorax ones (7th cervical and manubrium) or both acromions. The plane of projection was either the horizontal plane (HP), or the functional swing plane (FSP). An estimation of the shoulder/pelvis dissociation was performed based on markers of the acromions and pelvis. The thorax/pelvis based on the markers of the thorax and pelvis.
Outcome measures Economic endpoints The cost-effectiveness of the statin cessation strategy compared to continuation of statins will be estimated based on the incremental cost per quality-adjusted life year (QALY) gained at 36 months, from the perspective of the French healthcare system. QALYs will be measured using the Euro-QOL five-dimension, three-level (EQ-5D-3 L) questionnaire at inclusion and at each follow-up visit  [18]. This questionnaire has been validated in the French population. Healthcare resources and costs will be extracted from the French Administrative Health Care Database (Système National des Données de Santé, SNDS) by a merging procedure, operated by the French National Health Insurance Fund and using data from the SITE trial. The SNDS gathers all reimbursement data for 98.
Timeline and recruitment This study employs the research network of French universities' primary care departments (PCDs). Each PCD appoints a coordinating physician by geographic area to mobilise investigating physicians (486 expected in the study). The recruitment of GPs all across France maximises representativeness and limits the impact of potential local practices. The representativeness of the investigators is being documented by collecting data about their sociodemographic profiles, activity, and training. Patients are being recruited through consultations over a period of 36 months. Eligible patients not included in the study are being reported, along with the reason for non-inclusion.
Conduct of research Patients are approached and screened by GPs during their usual medical visit or on the renewal of their medication. Patients are first pre-included with their referent GP, and then after a time of reflection (that may be few minutes to days or months until the next visit), they sign the consent form with the GP and are randomized to one of the two strategies. On the consent form, participants are asked if they agree to use of their data should they choose to withdraw from the trial. Participants are also asked for permission for the research team to share relevant data with people from the Universities taking part in the research or from regulatory authorities, where relevant. This trial does not involve collecting biological specimens for storage. Table  1  outlines the different phases of the study and data collection.
Sample size According to data from the French National Institute for Statistics and Economic Studies (Institut National de la Statistique et des Études Économiques, INSEE) in the French population aged 75 years or older, we assume that mortality at 3 years in both study groups will be 15%. The SITE sample size was computed to achieve 90% power in showing the non-inferiority of statin cessation in comparison to statin continuation, assuming a 2. 5% unilateral α risk and a non-inferiority margin of 5% of the between-group difference in overall mortality at 3 years after randomization (nQuery Advisor software, v. 7. 0). A 5% non-inferiority margin is fairly commonly reported in the literature, but 3-year follow up is not. This yields an annual non-inferiority margin of only 1. 7%, which we think would be quite acceptable for the scientific community.
We do not have to deal with multiple tests, as cost-effectiveness analyses do not require statistical testing  [20]. We also assume that 20% of the individuals randomized to the statin-continuation group may spontaneously stop their treatment during follow up. Therefore, to ensure 90% statistical power in under-treatment analysis, it was decided to include 20% more individuals in the statin continuation group. In total, 2430 individuals will be enrolled in the SITE study, including 1080 in the statin ✓ ✓ ✓ ✓ ✓ MMSE b ✓ ✓ Consent signature ✓ Intervention ✓ ✓ ✓ ✓ ✓ Assessment SF12 and EQ-5D-3 L Questionnaires c ✓ ✓ ✓ ✓ ✓ Clinical examination d ✓ ✓ ✓ ✓ ✓ ✓ Biological tests e ✓(*) ✓ ✓ ✓ ✓ ECG f ✓ ✓ ✓ ✓ Clinical event data collection g ✓ ✓ ✓ ✓ ✓ Compliance h ✓ ✓ ✓ ✓ ✓ CVRF collection i ✓ ✓ ✓ ✓ ✓ SE/SAE collection j ✓ ✓ ✓ ✓ ✓.