dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses The study result was analyzed using the StatView 5. 1 software (SAS Institute, Cary, NC, USA). Each parameter was expressed as the mean±standard deviation. Data normality was checked using the Kolmogorov-Smirnov test. Our initial hypothesis that the PE score has a correlation with session intensity or with the level of patient autonomy was evaluated using the Pearson correlation. Since the results' values followed a normal distribution, an unpaired Student t-test was performed to determine if there was a difference in PE score according to the hemispheric location of the stroke. Even though the values followed a normal distribution, we still performed a Mann-Whitney non-parametric test to compare the median values of the two sub-samples from rank to rank, due to the particularity of the Borg Scale modified.
Data Collection One interviewer (N. G. ) collected data between March and May 2017. The interviews were carried out in a quiet room at the dental unit. The interviewer (a full-time final year dental student) was trained in qualitative methods and ensured the discussion did not stray off topic and led discussions in a conversational tone. All interviews were recorded digitally, and later transcribed fully by the interviewer. Data were collected until data saturation was reached, i. e. , when no new information was obtained  [14]. Indeed, as a qualitative study design, we aimed to reach an informationally representative sample (i. e. , not a statistically representative sample).
Data Management and Analysis We performed a thematic analysis, as described by Braun and Clarke  [16]. The process involved separating the transcripts into meaningful segments and assigning codes to the segments. Coding of the interview transcripts was carried out using Microsoft Word's comment feature. Codes and their corresponding passages were then regrouped into broad themes and displayed in an analytic matrix  [17]. Once the themes were identified, we described them in a text and illustrated them with transcript excerpts  [17].
Finally, some limitations should be mentioned. First, even though our search was extensive, we are aware that some material was not found due to the databases used (only Google Scholar and Google) or was in other languages. Our search, in fact, mainly captured the more standardized training tools, usually developed by big companies or organizations, which publish their training tools on the website, but we cannot exclude that other training tools, developed at local level, exist. Secondly, given the great heterogeneity of the training tools identified, in the analysis it was not possible to take into consideration all of the contextual features, such as the dimensions of the organization or the country where the training tools were carried out. Despite these limitations, the study also presents strengths.
CLM performed the data analysis and drafted the manuscript. RT performed the data collection and contributed with project creation. TR and RB contributed with project creation. All authors discussed the results and participated in the revision of the manuscript. Additionally, we would like to thank  Vincent Ballesio  for his assistance during the data collection. This work was supported by the  Fondation pour la Recherche Médicale  (grant number  FDT20170437383 ); the  Ecole Normale Supérieure de Paris ; and the  Agence Nationale de la Recherche  (EquiSim, grant number  ANR-16-CE19-0006-01 ).
The upload of coded data to a central database required additional information in 14/16. In 4/16 the NCs had to ask separate approval to keep a subject identification code list to de-pseudonymize the patients if questions would occur. Only 2/16 of the NCs agreed that informed consent was necessary for this observational study. Overall, 6/16 of the NCs were satisfied with the entire process and 8/16 were (very) unsatisfied. 11/16 would welcome a European central EC that would judge observational studies for all European countries. Discussion: Variations in the process and prolonged time needed to get EA for observational studies hampers inclusion of patients in some European countries. This might have a negative influence on the external validity. Further harmonization of ethical approval process across Europe is welcomed for low-risk observational studies.
We recently performed a multinational, observational study of very old critically ill patients (the VIP1study)  [4]. The goal of the VIP1-study was to analyze whether frailty was associated with poor outcome in this elderly patient group. Performing this research provided us with an opportunity to explore how differently ethical approval processes are working in various countries. The VIP1-study is prototypical for low-risk observational research. In short, in this study we included 5021 patients over 80 years old from 311 ICUs. We looked at reasons for admission, severity of disease, frailty, treatments during ICU (e. g. mechanical ventilation, renal replacement therapy, and vasopressor treatment), limitations in life sustaining therapy and outcome at 30 days. The data was collected in central online database and apart from age, gender and study number no other data was collected that could lead to identification of the patients.
Results Half of the countries (n = 8/16) needed apply to only one national medical ethical committee (national EC), while the others had to apply to the national ECs and subsequently to the individual hospital institutional research boards (IRBs) (question 1). The most frequent body to be approached (question 2) was a local hospital institution review board (IRB) (67%), followed by a regional EC (33%) and a national EC (25%). In one country all participating hospitals had to send individual applications to their respective IRBs since there was no national medical ethical committee. Most hospitals had to provide more specific information regarding the upload of data (question 5) to a central database abroad (n = 14/16). One country had to apply for this at a separate EC, because the national EC was not allowed to judge data collection in a foreign country.
Ofsthun and Abdelwahed 122 introduced a Time Failure Propagation Graph (TFPG)-based reasoner for real-time failure diagnosis and applied the method on fuel systems in single and multiple failure scenarios. Long and Wang,  123  Lopez and Sarigul-Klijin,  124  Chen and Chen 125 investigated fault diagnosis expert systems for the aircraft fuel system, the structural damage and embedded airborne electronic equipment, respectively. Prognostics is conducted in the PA block, the output of which is the health state at future times, or the RUL of a component or system. In literature, various prognostics tools are developed based on AI techniques such as ANNs and Dynamic Bayesian Networks (DBNs). Brotherton et al. 126  developed a fault prognosis technique combining ANNs and automated rule extractors for gas turbine engines, and applied the technique on a dataset of operating engines.
ARP6461 provides guidance on the definition, development, V&V, and certification of SHM. According to Ref. 134  , an SHM system, the Comparative Vacuum Monitoring (CVM) system, which monitors the integrity of a structure and detect a crack before it propagates to the critical length, has been approved by Boeing. A real-time monitoring and forecasting software was used in the static test of ARJ21, which is able to monitor the strain and displacement data through distributed monitors, and calculate the structure stress and safety margin in real time with high accuracy and reliability. 135 esides, Boeing also has successfully implemented IVHM on their planes. For example, in Boeing 737, Boeing 757, Boeing 767 airplanes, Boeing uses the Aircraft Condition Monitoring System (ACMS) to collect the data needed to support critical health management programs, such as Flight Operation Quality Assurance (FOQA) and ECM.
Clinical samples A first clinical sample composed of 28 clients entering the first session of psychotherapy was recruited through four Psychologists (3 women, 1 man), who showed interest in participating to our study. Initially seven therapists showed an interest to participate in our study. Both psychologists and their clients were fully informed about the protocol and signed respectively an electronic informed consent for clients, and a paper-pencil version for a therapist. Data have been securely surveyed online at each session on practitioners' device (computer or tablet) by collecting non-identifying data and attributing a code to follow each participant. Neither the investigator had access to client identity, nor the psychologist accessed to client records, rendering a non-feedback condition (NFC).
2% patients presenting concurrently anxiety and substance abuse issues, 20. 8% came for other reasons including health/somatic issues and personality disorders. 37. 5% of clients were receiving psychotropic drugs. Available client's session ranged from 1 to 9. Therapists were advised to stop recording if the client was returning to care after duration superior to 8 weeks since his/her last session (12. 50 % of cases). Patients dropped out of their treatment in 37. 50% of cases; at the end of the data collection remaining patients were either still in treatment (25%) or finished therapy (25%). A second clinical sample (n = 28) composed of patients from the first author outpatient site practice, who began their first episode of treatment using the PCOMS French unified version, qualifying the research for a multiple study.
RUNNING HEAD: VALIDATION OF THE FRENCH VERSION OF TWO BRIEF The first author used the LimeSurvey platform to render administration mostly similar in computerized presentation to paper-pencil, to secure and to standardize web administration. In these three samples, only complete data for each session record were investigated (if the participant filled out all the questions related to the session). Non-feedback samples data (clinical and non-clinical) came from a doctoral project and from routine clinical practice.
Receiver Operating Characteristic Curve (ROC) ROC curve has been drawn in SPSS by using the biggest sample of patients at session 1 (TCS, clinical population, n = 56) and the community sample (NCCS, n = 130). The total area under the curve was. 77, the score of 23 (22. 92) rendered a fair balance between specificity and 1-sensitivity (respectively. 70,. 25); this is lower at one point than cut-off (c), lower than the US cut-off (25;  Miller et al. , 2004 )  and lower than Dutch Cut-off (24;  Janse et al. , 2014).
Acknowledgements This research did not receive funding. The first author wants to especially thank the comity composed of clinicians from  Valoris for Children and Adults of Prescott-Russell Agency in Canada  ( Raymond Lemay ,  Chantal Tassé ,  Madeleine Lalonde ,  Nicholas Cardinal ), and  John Deltour  ( Belgium ) who worked to develop a unified French version of the measures. We also want to thank  Charlotte Dedenis  for her English editing.
Data Recording Two force platforms (AMTI, OR6-6, Newton MA, USA) recorded the forces under each foot and allowed the calculation of the total center of pressure (CoP) position. Reflective markers were attached bilaterally over key landmarks of the leg and trunk (on the medial and lateral malleolus, calcaneus, lateral first and fifth metatarsal head, top of second metatarsal head, medial and lateral femoral condyles, and bilateral acromion) to record foot, shank, and trunk movements using an eight-camera motion analysis system (QTM-Qualisys Tracking Manager, Qualisys, Gothenburg, Sweden). Perturbation, kinetic, and kinematic data was collected in Qualisys at 100 Hz for 15 s, beginning at least 1 s prior to perturbation. Kinematic data was filtered using a second order Butterworth filter with 1 bidirectional pass resulting in a fourth order filer.
Differences between legs were assessed using a Wilcoxon signed-rank test in SS conditions in controls, whereas for post-stroke participants, we compared stepping performance between the legs for the same body weight distribution condition. Thus, steps with the lessinvolved leg in the N-PAS condition (i. e. , less weight on the stepping leg) were compared to steps with the more-involved leg in the PAS condition. The IBM SPSS 23 was used for all statistical analyses. An alpha level of 0. 05 was used to test statistical significance.
Method The PubMed, Cochrane Library, Science Direct and Embase databases were searched without language restriction on April 2019, with the following keywords: suicide* AND (« health care worker* » OR physician* OR nurse*). When possible, we stratified results by gender, countries, time, and specialties. Estimates were pooled using random-effect metaanalysis. Differences by study-level characteristics were estimated using stratified metaanalysis and meta-regression. Suicides, suicidal attempts, and suicidal ideation were retrieved from national or local specific registers or case records. In addition, suicide attempts and suicidal ideation were also retrieved from questionnaires (paper or internet).
Search strategy and study eligibility We reviewed all studies involving suicides, suicide attempts or suicidal ideation in health-care workers. Students were excluded because of the difference in responsibilities in comparisons with health-care workers, and because of the existence of previous recent meta-analyses focusing specifically on health-care students  [21, 22, 23, 24] ; we included interns because they were not included in the aforementioned meta-analyses on prevalence of suicides, suicide attempts or suicidal ideation, and because they could have similar responsibilities to senior practitioners. The PubMed, Cochrane Library, Science Direct and Embase databases were searched on April 2019, with the following keywords: suicide � AND (« health care worker � » OR physician � OR nurse � ).
The search was not limited by years or languages. To be included, articles had to be peer-reviewed and to describe original empirical data on suicides, suicide attempt or suicidal ideation in health-care workers. When data were available, we also collected data from a control group (such as general population) for comparisons purposes. In addition, reference lists of all publications meeting the inclusion criteria will be manually searched to identify any further studies not found through digital research. The search strategy was presented in Fig 1. Three authors (Claire Aubert, Valentin Navel and Frederic Dutheil) conducted all literature searches, and separately reviewed the abstracts and decided the suitability of the articles for inclusion. Two others authors (Bruno Pereira and Martial Mermillod) have been asked to review the articles when consensus on suitability was debated.
Statistical considerations Statistical analysis was conducted using Comprehensive Meta-analysis software (version 2, Biostat Corporation)  [28, 29, 30]  and Stata software (version 13, StataCorp, College Station, US)  [28, 29, 31]. Main characteristics were summarized for each study sample and reported as mean (standard-deviation) and number (%) for continuous and categorical variables respectively. Statistical heterogeneity between results was assessed by examining forest plots, confidence intervals (CI) and using formal tests for homogeneity based on I 2 statistic, which is the most common metric for measuring the magnitude of heterogeneity between studies and is easily interpretable. I 2 values range between 0% and 100% and are typically considered low for <25%, moderate for 25-50%, and high for > 50%.
Random effect meta-analysis (DerSimonian and Liard approach) were conducted when data could be pooled  [32]. P values < 0. 05 were considered statistically significant. We conducted: 1) meta-analyses on the Standardized Mortality Ratio (SMR) for suicides i. e. the ratio between the observed and expected number of. Effect-size was estimated for quantitative endpoints as number of physicians having done suicide attempt and number of physicians with suicidal ideation. A scale for ES has been suggested with 0. 8 reflecting a large effect, 0. 5 a moderate effect, and 0. 2 a small effect  [33]. When possible (sufficient sample size), metaregressions were proposed to study relation between prevalence and epidemiological relevant parameters determined according to the literature: sex, geographic zone, epoch of time (for studies with a follow-up over several consecutive years, we based our statistics on the mean year of epoch of time).
In March 2014, a laboratory unit of the European Mobile Laboratory (EMLab) consortium was deployed to the EVD treatment center (ETC) in Guéckédou, Guinea, to provide laboratory diagnostic service  [15]. In response to the increasing number of EVD cases in western Guinea, the unit was relocated to the ETC in Coyah, where it was operational from February to December 2015. During this period, EMLab tested 7000 samples by an Ebola virus (EBOV)-specific reverse transcriptionpolymerase chain reaction (RT-PCR) assay. Here, we report the analysis of the laboratory data generated in Coyah and the results of a retrospective study aiming to assess associations between potential risk factors, compassionate use of favipiravir, and EVD outcome.
Diagnostic Assays All laboratory data were generated by the EMLab unit at the ETC in Coyah. Viral RNA was extracted with the QIAamp Viral RNA Mini Kit (Qiagen) from whole blood specimens mixed with EDTA, other body-fluid specimens, or swab specimens. EBOV RNA was detected using the RealStar Filovirus Screen RT-PCR kit, version 1. 0 (until March 2015; Altona Diagnostics), or the RealStar Zaire Ebolavirus RT-PCR kit, version 1. 0 (from March 2015 onward; Altona Diagnostics), using the SmartCycler II system (Cepheid; until June 2015) or the Rotor-Gene Q system (Qiagen; from March 2015 onward). Although these assays slightly differ in terms of sensitivity, they produce virtually identical cycle thresholds (Cts)  [20]. Cts were reported to clinicians as a semiquantitative measure of viral load.
Data Management Demographic patient data was provided on the laboratory request forms accompanying the samples by hospital staff, contact-tracing teams, and other partners in the field. Patient name, age, sex, residence, ETC patient identifier, sample identifier, sample type, collection date, date of symptom onset, EBOV RT-PCR result (with the corresponding Ct), and malaria RDT result were captured in the EMLab database (Excel, Microsoft) and reported to national authorities and the World Health Organization (WHO) on a daily basis. To facilitate the assignment of multiple samples to individual patients, validate the demographic information, and document the outcome, the EMLab database was manually merged with the Guinean database of patients with EVD, maintained at the WHO country office in Conakry.
Patient names and sample identifiers recorded in both databases were used as primary identifiers for merging; additional variables were used to verify the match. Inconsistencies between the 2 databases and between sample entries for the same patient were resolved, and the data were cleaned using Stata 14 (StataCorp). Patients were classified into 4 main categories: (1) patients with suspected EVD who attended an ETC and had negative results of EBOV RT-PCR analysis, (2) patients with EBOV RT-PCR-confirmed EVD admitted to the ETC, (3) patients who had negative results of EBOV RT-PCR analysis and died in the community, and (4) patients with EBOV RT-PCR-confirmed EVD who died in the community (Table  1 ). Individuals who could not be assigned to any category because of missing or conflicting data (n = 151) were excluded from further analysis.
To analyze the association between independent variables and outcome by using statistical methods, the cleaned EMLab/ WHO database was manually merged with the favipiravir treatment database. The merged database comprised 286 patients with EVD, of whom 99 (35%) had been treated with favipiravir. Patients were selected for the retrospective study according to 6 inclusion/exclusion criteria, specified as follows (Supplementary Figure  1 ). First, children <1 year of age, who had not been eligible for compassionate use of favipiravir, were also excluded from the nontreatment group, to maintain comparability between favipiravir-treated and untreated patients (7 patients were excluded). Second, patients who received ZMapp in combination with favipiravir (12 patients) or were treated with IFN-β1a (9 patients) were excluded.
Third, the EVD diagnosis including the Ct had to be confirmed by EMLab in the first specimen collected from the patient (40 patients were excluded because of missing EMLab test results for the first specimen). Fourth, the first specimen tested had to be a blood specimen (11 patients were excluded because testing was done on a swab specimen). Fifth, owing to the above criteria, favipiravir-treated patients from 2 centers only, ETC Coyah and ETC Forécariah, remained in the data set. To maintain comparability between favipiravir-treated and untreated patients, the analysis was confined to the ETCs in Coyah and Forécariah (20 untreated patients were excluded because of admission to various other centers). Sixth, EVD cases were admitted to the ETC on the same day the diagnostic sample was collected (median, day 0; interquartile range [IQR], days 0-0), and treatment with favipiravir was commenced the day after (median, day 1; IQR, days 0-1).
5% (31 of 73) and 42. 3% (11 of 26), respectively; the CFR among nontreated patients included in and excluded from the study was 57. 8% (52 of 90) and 56. 7% (55 of 97), respectively. We verified with the records that all eligible patients had the first specimen collected before treatment was commenced. In addition, all Cts were verified using data recorded by the realtime PCR machines.
Analysis of Risk Factors Associated With Outcome We selected 163 (57%) of 286 patients with laboratory-confirmed EVD for a retrospective observational study to assess associations between potential risk factors, the effect of favipiravir treatment, and outcome. The criteria for selecting the study subjects aimed to eliminate or reduce various biases in the raw data collected under nonstandardized conditions in the field (Supplementary Figure  1 ). The CFR among study patients was 51% (83 of 163) and thus representative of the whole data set (52%). In addition to Ct and age, which were available for all patients, clinical chemistry parameters had been measured on admission in a fraction (40%-44%) of the 163 patients (Supplementary Figure  10 ). Fatal outcome was associated with a lower Ct (P <.
DISCUSSION We present an analysis of laboratory, demographic, treatment, and outcome data collected during the 10-month EMLab operation in Coyah. Compared with the first site of EMLab operation in Guéckédou, in the region of Forest Guinea, the much higher number of samples tested from patients who died in the community is apparent, with 3823 tests in Coyah versus 563 in Guéckédou  [15]. This resulted from intensified active surveillance and contact-tracing efforts that were implemented during the later phase of the epidemic. However, although just 2% of patients (84 of 3823) who died in the community had EVD diagnosed, this number still accounted for 36% (84 of 233) of all fatal EVD cases diagnosed by the laboratory in Coyah (149 in the ETC plus 84 in community; Table  1 ). Thus, more than a year after the outbreak started in Guinea, these estimates suggest that one third of all patients with EVD in this region did not attend the ETC and died in the community, despite major efforts to increase awareness in communities.