dataesr/scientific-paragraphs-categorization huggingface dataset:
11 In contrast, based on data from the Finnish FinDM database, the average adherence rate to statins over w3 years in patients with type II diabetes in the realworld setting was reported to be 57%. 11 Similarly, in a retrospective cohort study using the Saskatchewan Drug Plan and Extended Benefits database in Canada, adherence to statins was 52% in the first year and 44% by the third year. 39 Real-world data on adherence to ezetimibe capsules are limited; however, 1 retrospective study investigating oral LLTs, including the combination of ezetimibe and simvastatin, in a managed care setting demonstrated that mean proportion of days covered fell below the threshold for adherence (80%) even within the first 3 months of therapy. 40 This contrasts with the. 80% to 97% adherence rates observed with ezetimibe plus statin therapy at within the first 3 months of treatment in clinical trials.
41, 42 The introduction of injectable agents for asymptomatic conditions, such as hypercholesterolemia, has been met with uncertainties regarding patients' willingness to self-inject and maintain the injection schedule over the long term compared with taking oral drugs on a daily basis. Results from this and a previous study  27  suggest that acceptance of and adherence to injectable alirocumab is high in clinical trials. However, they also raise the question of how  adherence to a therapy requiring Q2W injections (26 injections per year) and adequate refrigeration compares with that of an oral drug that is given daily (365 administrations per year) and does not require refrigeration in the realworld setting. Open-label extension studies, such as ODYSSEY OLE (NCT01954394), are ongoing. In these studies, patients will have the opportunity to observe the impact of their treatment, which may consequently impact adherence levels.
Adherence to alirocumab injections was evaluated over a period of 1 to 2 years using pooled data from 6 Phase III, randomized, double-blind, controlled ODYSSEY trials (FH I [NCT01623115], FH II [NCT01709500], HIGH FH [NCT01617655], COMBO I [NCT01644175], COMBO II [NCT01644188], and LONG TERM [NCT01507831]; Fig.
Acknowledgments Authors' contributions: M. F. , H. M. C. , and J. G. R. were investigators and/or steering committee members for the alirocumab  ODYSSEY Phase III program  and were involved in the concept, design, and data collection for the 6 trials included in this pooled analysis; together with W. J. S. and J. M. E. , they contributed to the design of the current analysis. Statistical analyses were performed by  G. A. All  authors contributed to the interpretation of the data, critically revised the article throughout development for intellectual content, approved the final version and are accountable for the accuracy and integrity of the work.
Introduction The EuroHYP-1 trial is a multicentre, randomised, superiority phase III international clinical trial with masked outcome assessment testing the benefits and harms of therapeutic cooling in awake adult patients with acute ischaemic stroke (website: http://www. eurohyp1. eu). The trial is designed according to the SPIRIT guidelines, and the background, design, and rationale have previously been published  [1]. The EuroHYP-1 trial protocol has been available online on www. ClinicalTrials. gov since the start of the trial on 4 April 2013. The trial is endorsed and supported by European Clinical Research Infrastructures Network (ECRIN) (www. ecrin. org). Here we describe the updated detailed statistical analysis plan that has been finalised while data collection in the EuroHYP-1 trial is underway, and to which all data analyses in the main publication of the EuroHYP-1 trial results will adhere.
A detailed statistical analysis plan was part of the protocol, and the Steering Group of the EuroHYP-1 trial unanimously approved the statistical analysis plan on 30 March 2016. The present amendments of the original statistical analysis plan focus on the primary and the secondary outcomes. The amendments have been made in order to make the analysis more concrete and transparent. Patient recruitment of 800 patients is expected to be completed, and the final follow-up is predicted to occur in 2018-19, after which the database will be locked and data will be analysed.
The sample size Originally, we planned to demonstrate or reject an absolute difference of 7% between the intervention groups (equivalent to an odds ratio (OR) of 0. 74) and allowing for 3% loss to follow up in a sample size of 1500 randomised patients with a type 1 error risk of 5% and a type 2 error risk of 10% (EuroHYP-1, EudraCT number 2012-002944-25, version 2, 20 December 2012). Due to exceptionally slow enrolment, we realised that this target was no longer realistic. Accordingly, in 2014, the sample size was downgraded to 800 patients based on considerations explained in version 4 of the protocol (EuroHYP-1, EudraCT number 2012-002944-25, European Database on Medical Devices (EUDAMED) number CIV-12-09-008821, planned trial period July 2013 to March 2017 (45 months), version 4, date 29 June 2015).
15 Only selected study sites. 16 Informed consent will be obtained in accordance with national regulatory requirements. 17 NIHSS assessment at End of Hour 24 ± 2 hours. 18 Starting at t = 30 min. 19 Every 60 minutes only. 20 Prior to intended repeat administration of pethidine. CT computed tomography, ECG electrocardiogram, EQ-5D-5 L EuroQoL quality-of-life scale, GCS Glasgow coma scale, MRI magnetic resonance imaging, NIHSS National Institutes of Health Stroke Score, SAE serious adverse event, WHODAS World Health Organization Disability Assessment Schedule the opposite. After two abstracts with two conclusions have been written and both accepted by the Steering Group, the code will be broken. The primary analyses of primary, secondary, and exploratory outcomes will be those of the intention-totreat population adjusted for the protocol-specified stratification variable and the minimisation variables, and if necessary using data sets generated using multiple imputation.
Brain infarct size 48 ± 24 hours after randomisation: assuming a minimal relevant difference of 2 ml, a SD of 15 ml  [10] , an α of 0. 05, and a sample size of 250 participants, the power may then be calculated to 0. 18. Accordingly, this outcome will be exploratory. Number of patients with at least one serious adverse events 91 ± 14 days after randomisation: assuming a control group event proportion of 20% of patients with at least one SAE, an intervention group event proportion of 10%  [11] , an α of 0. 05, and a sample size of 800 participants, the power may then be calculated to 0. 98. Mean number of SAEs per patients: As we do not have prior data to indicate a probable minimal relevant difference, we cannot calculate power for this outcome. These data will be analysed as an exploratory outcome.
This research was supported by grant ANR-10-BLAN 0112  (Laraki). when firms choose the same price, location, bid or acting time. The objective of this paper is to extend and link conditions under which Nash, approximate and sharing rule equilibria exist in such games. Nash equilibrium is a strategy profile where each agent is reacting optimally to other players' plans. Mathematically, it is a fixed point of the best-response correspondence. When payoff functions are continuous and quasiconcaves, an application of Kakutani's fixed point theorem leads to the Nash-Glicksberg theorem  (Glicksberg (1952) ;  Nash (1950a,b) ). In discontinuous games, the Kakutani approach cannot be directly applied because a player may have no optimal reply or because his best choice jumps as a function of the choices of the other players.
The variability in the operational definitions might be acceptable from a public health perspective, because the choice of an instrument should largely rely on the intervention to put eventually in place, the available resources and clinical priorities, factors that are extremely variable across settings and regions. At the same time, the identification of a gold standard measure might still be important to obtain because it represents the only way for providing to the construct of a nosological entity to the often ambiguous frailty condition  [12]. To our knowledge, the frequency in the use of tools for the assessment of frailty and the components of this geriatric syndrome has never been estimated. Consequently, we designed an online survey with the objective to collect data looking at the proportion of clinicians assessing frailty in their daily practice and at the tools used by these clinicians.
International Society for CNS Clinical Trials and Methodology convened an expert working-group that assembled consistency/inconsistency flags for the Positive and Negative Syndrome Scale (PANSS). Twenty-four flags were identified and divided based on extent to which they represent error (Possibly, Probably, Very probably or definitely). The flags were applied to assessments derived from the NEWMEDS data repository and the CATIE clinical trial data. Almost 40% of ratings had at least one inconsistency flag raised and 10% had two. Application of flags to clinical rating can improve reliability and validity of trials.
To examine the incidence of such flags, they were applied to (a) the NEWMEDS database, which is composed of 121,635 assessments of 19,489 subjects from placebo controlled trials of second-generation antipsychotics (for a description see  (Rabinowitz et al. , 2014) ) and (b) the 8849 assessments of 1450 subjects in CATIE (For a description see  Lieberman et al. (2005) ).
Discussion The potential errors identified by flags do occur and the frequency is remarkably consistent between the NEWMEDS and CATIE data sets despite the differences between the two in terms of frequency of assessment. The most common flag in both data sets is a rating of tension in the absence of anxiety. This violates a PANSS rating rule. Almost as frequent in both data sets is lack of consistency between high levels of hallucinatory behavior and preoccupation. All other flags occur 5% or less of the time. There are limitations to this approach. The first is that nearly all of these data patterns could happen in a real subject reporting legitimate experience. However, based on the NEWMEDS and CATIE data, each one is unlikely to happen (most are exceedingly unlikely to happen). Therefore, in general, if more than one of the flags is present, it is likely that the assessment should be questioned.
Acknowledgments The research leading to these results has received support from the  Innovative Medicine Initiative Joint Undertaking  under grant agreement no. 115008  of which resources are composed of  European Federation of Pharmaceutical Industries and Associations (EFPIA)  in-kind contribution and financial contribution from the  European Union 's  Seventh Framework Programme  (FP7/2007-2013). Funding source was not involved in the collection, analysis, and interpretation of data; in the writing of the report; and nor in the decision to submit the paper for publication.
To this end, we (1) conducted a European-scale survey among ecologists to assess potential biodiversity effects of EFA options; (2) collected data on farmers' uptake to examine onthe-ground EFA implementation; (3) synthesized expert opinions and a review of the factors influencing farmers' decisions; and (4) compared EFA options according to their impacts on biodiversity and their relevance for farmers to identify possible improvements of EFA design and implementation. Through this interdisciplinary and European-scale approach, we develop recommendations aiming to increase the uptake and best management of biodiversity-friendly options by farmers; reduce administrative burdens; and promote coherence between CAP and EU's nature conservation goals.
Statistics on the implementation of EFAs in 2015: We asked agricultural ministries across MSs for data on the number of farmers and total area registered under each EFA option in 2015. We considered that these uptake levels represent good indicators of farmers' preferences. We received data from eight MSs: Germany, Estonia, The Netherlands, Denmark, Austria, Czech Republic, England, and Poland as well as a preliminary data at the EU level based on all MSs except France (EC 2016b). We defined EFAs as "win" (+) and "lose" (-) based on high or low uptakes at the EU level, assuming that these indicate attractiveness from the farmers' perspectives. This assignment was confirmed through interviews with farmer representatives in a complementary study  (Zinngrebe et al. submitted). We obtained additional data on variations in farmers' uptake levels across German federal states, including (a) the total cover of each EFA option declared in 2015 compared to the total Utilized Agricultural Area (UAA), and (b) the total cover of nitrogen-fixing crops and fallow land over time since 2004.
2000). (3) Set targeted and clear management requirements Ecological experts provided a wide range of specific recommendations to improve the effectiveness of EFA options (for an overview, see SI 2 The European Commission so far has not published any full or comprehensible dataset on EFA-uptake at the MS level (for example, we used data primarily from cooperating ministries). Greater transparency, by publishing the implementation data yearly, can promote learning and improvements, as well as cooperation among stakeholders.
g. , discrimination, stigmatization, over-medicalization)  (13). Finally, it may improve the design of clinical trials and the interpretation of their results. For example, excluding subjects whose cognitive function or frailty levels are unlikely to decline over time may increase the effect size of potentially effective interventions. As of today, this point seems of crucial interest, considering that nearly 180 RCTs are currently recruiting subjects with frailty and/or MCI worldwide (source: www. clinicaltrials. gov; search updated in August 2017). Several hypotheses may be proposed to explain the observed remission of the considered at-risk conditions  (13). First, it may simply be due to the wrong classification of subjects participating in the studies, with either normal individuals misdiagnosed as frail/MCI at enrollment, or with frail/MCI subjects misclassified as normal at the end of the observation period.
1. The type of our research Semi-directional interviews were conducted and questionnaires were administered in order to propose a modeling of the motivations of women social entrepreneurs engaged in the professional integration of low-skilled women in Lebanon. The information will be presented in the following Business Management by analyzing the qualitative and quantitative data resulting from interviews and questionnaires. Concerning the type of information gathering, we carried out a clinical research by interviewing women social entrepreneurs to collect the necessary data  [30]. A pilot interview was conducted with the women social entrepreneur Maud Jabbour, which promotes the professional integration of low-skilled women through its "women to women success" business. This interview allowed us to partially elaborate the questions of our semi-directive interview.
We interviewed the six women on the motivations that led them to create a social enterprise. The interviews were carried out by telephone during the months of July and August 2015. The choice of the telephone interview is explained by the agenda of the interviewed women entrepreneurs. We proposed to conduct face-to-face interviews, but respondents preferred telephone interviews. The duration of each interview varies between 40 and 45 minutes. The six interviews conducted thus allowed to generate four and a half hours of work in the field. The processing of the qualitative data was done manually by comparing the speeches of the interviewees. Quantitative data, through a questionnaire administered face-to-face and by telephone to 57 women employed by the six women social entrepreneurs, supplemented our qualitative data.
We have begun administering the questionnaire on June 28, 2016 until August 5, 2016. Given that our study is exploratory, we have chosen an almost equal number of employees. There are 10 employees from each company and seven employees from Mrs T. B company's who has only 7 employees. Concerning the analysis of the quantitative data, simple frequency analyzes followed by the variance test "Anova" via SPSS 17 were carried out. The Anova test can be performed when comparing several samples of the same size or not. In our case, there are six groups or six quasi-equal samples. The Likert scale with five point scale from "strongly disagree" to "strongly agree" was used at the level of independent variables.
Methods: We will include publications from 2004 to 2016 that target four concepts: polypharmacy, older individuals, multimorbidity and positive/negative outcomes. The search will be performed using EBM Reviews, Embase, Global Health, MEDLINE, AgeLine, CINAHL, Health Policy Reference Center, Public Affairs Index, SocINDEX and Google Scholar. Two independent reviewers will screen the articles, extract the information and evaluate the methodological quality of included studies. The results will be presented in tables and narrative summaries will be performed. We will perform meta-analyses (objective 3) if the heterogeneity is not important. Discussion: This review will help describe the various ways of conceptualizing polypharmacy and how it is associated with health outcomes. We have selected outcomes most relevant for public surveillance performed with administrative databases.
Participants/population The review will consider studies that include people aged 65 years and older with at least two concurrent chronic diseases. We will include studies if at least one of the following applies: At least 80% of participants are aged 65 years and older. The data from people aged 65 years and older can be extracted. We will include all settings (community, hospital, nursing homes) and types of health care (public, private). We will perform subgroup analyses according to those settings.
Outcomes Objective 1: We will include articles presenting a clear operational definition of polypharmacy. Objective 2: We will include studies that quantify the association of predisposing or concurrent factors (e. g. demographic, treatment-related, morbidity, health system-related) with the presence of polypharmacy. Objective 3: We will include studies that evaluate the following outcomes of polypharmacy: We will also search Google Scholar and Google to identify grey literature, such as governmental reports, reporting on indicators of polypharmacy that have been used for population-based monitoring. Four concepts have been defined in our search: polypharmacy, older individuals, multimorbidity and positive/ negative outcomes. The search strategy has been adapted to the syntax requirements of each database (use of different thesaurus terms, truncation and wildcard characters).
Data extraction Two independent reviewers (AZ/NSD, CS) will conduct a full-paper evaluation and data extraction. Established data extraction forms adapted for the three objectives of the review have been created using FileMaker MD (https://www. filemaker. com). This will allow all researchers to have access to the data in real time. For each study, we will record bibliographic details, type of study, context, participants and the definition of polypharmacy. Duplicate studies (articles related to the same specific study) will be excluded (criterion 10). We will record specific information according to each objective: Objective 1: We will extract details about the definition of polypharmacy, the types of medications included in the definition, the way the medications have been counted or what qualitative criteria have been used in order to label a medication regimen as polypharmacy (e.
Risk of bias (quality) assessment For objectives 2 and 3, two independent reviewers (AZ and (LG, MLL, EK, DC, ND)) will assess the quality of the studies according to the Scottish Intercollegiate Guidelines Network methodology checklists for controlled trials, cohort studies and case-control studies  [20] [21] [22]. In case of disagreement between two reviewers that cannot be resolved through discussion, a third reviewer will be consulted to reach consensus (CS). The studies will be rated according to a nominal scale of risk based on the gathered information (critical, serious, moderate or low risk). The quality assessment data will be presented in the table of the results. All data will be interpreted in light of the risk of bias. Subgroup analysis based on quality will be performed if required.
We will strive for ensuring that complete information be available for all eligible studies, notably by contacting authors for all abstracts and full text that do not provide required data. Nonetheless, we anticipate that some authors will not answer our queries, which will limit the inclusion of recent evidence into our review. Finally, we have selected a limited number of outcomes for the third objective. Those outcomes should be the most relevant for population surveillance as they can be tracked in administrative databases. However, other important outcomes for clinical practice, such as adverse events and adherence, will not be addressed. Our review will include a great number of observational studies. This poses challenges because it entails ensuring the associations observed are not biased (e.
To the best of our knowledge, there is no study yet that has analyzed the impact of diversification on bank performance and risk within the Tunisian context. Therefore, this paper tries to fill the gap of literature by providing a comprehensive study. In the empirical study, we used annual data related to 20 Tunisian banks during the period 2005-2015 and we conduct an econometric model based on the General Moments of Methods in system (SGMM). Empirical results indicate that the main determinants of non-interest income are relatives' performance such as RROA and RROE, some bank specifics (bank size and loan specialization), technological innovation (ATM) and credit cards. We also find that diversification increases bank performance for both ROA and ROE measures. For the effect on the level of risk, non-interest income appears to be negatively and significantly correlated with the standard deviation SHROA and SHROE.
Their results support that bank characteristics and the ATM technology as the most influential factors shaping the trend of non-interest income in the banking industry in Barbados. Findings also indicate that non-interest income is positively related to both bank profitability and earnings volatility. Hakimi et al. (2012)  analyzed the determinants of the noninterest income for the Tunisian context by the use of data of 10 Tunisian retail banks. The sample is observed during the period 1998-2009. Results of panel data regression reveal that the advance of information and communication technologies (ICT) proxied by the number of automatic teller machine and the number of credit cards affect positively the level of NII. Findings also indicate that banking characteristics such as the bank size, the credit quality and the banking strategy are considered as the main influential factors shaping the tendency of non-interest income in Tunisia.
Using a dataset of 662 relatively large commercial banks in 29 OECD countries during the period 1992 to  2006 , Hahm (2008) )  analyzed the determinants and consequences of the NII. Empirical results show that banks with relatively large asset sizes, low net interest margins, high impaired loan ratios, and high cost-income ratios tend to exhibit higher non-interest income shares. Besides bank specifics the author tested the effect of macroeconomic factors on the level of NII. Findings indicate that banks showing slow economic growth, a stable inflation environment, and well developed stock markets tend to show higher non-interest income shares. De  Young and Hunter (2003)  and De Young et al. (  2004 ) also report that bank size is positively associated with the level of non-interest income.