dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Strains were obtained from the  Caenorhabditis Genetics Center (CGC) , supported by the  NIH Office of Infrastructure Programs  ( P40 OD010440 ), and the  National BioResource  Project. We acknowledge the  Discovery Platform and Informatics Group at EDyP  ( U1292 INSERM/CEA/UGA ,  Institut de Recherche Interdisciplinaire de Grenoble, IRIG) , and the  SFR Biosciences  ( UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL ) : PLATIM. We would like to thank  Dr. Cécile Bedet ,  Laura Faccin  and  Lisa-May Alvarez  for technical support. This project is supported by the  Agence Nationale de la Recherche (ANR)  [ 15-CE12-0018-01 ] and [ ANR-19-CE12-0025 ] and the  Centre National de la Recherche Scientifique (CNRS). The proteomic experiments were partially supported by  ANR  under projects  ProFI (Proteomics French Infrastructure ,  ANR-10-INBS-08 ) and GRAL, a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ).
Analysis All responses from completed surveys were entered in Jisc Online Surveys® and exported to Microsoft Excel® for descriptive statistics. Responses in Welsh were translated by a Welsh speaker before entering into a spreadsheet. A 10% validation check was completed to quality assure the data transfer. Free-text comments were imported to Microsoft Word® and content as well as inductive thematic analysis was used. Missing values were reported. Responses to Likert type questions were summarised using counts and proportions. Characteristics (age, gender, tried to see a GP before) of patients who attended a STTT consultation within the same period were obtained from Choose Pharmacy, the national IT platform via which services are recorded in community pharmacies in Wales. Since the Choose Pharmacy data were anonymised, the patients responding to the survey data could not be identified in this dataset to enable us to describe two mutually exclusive samples (patient responding to the survey versus patients that did not).
RESULTS A total of 510 completed surveys were received by the end of the data collection period (510 out of 2,839 consultations, response rate 18%) (English n=499, Welsh n=11). No surveys were returned electronically. Table  1  compares patient demographics of the survey responders with the whole population who received a STTT consultation in a community pharmacy participating in the pilot, during November 2018 to May 2019. The age and gender distributions are similar between the two populations, but a greater proportion of those responding to the survey had seen their GP previously.
12 High levels of satisfaction were reported and pharmacists highlighted the service's role in providing patient education and contributing to principles of antimicrobial stewardship. Factors that would empower them to deliver the service confidently were described. A second study investigated data derived from a range of national databases providing pharmacy, GP, prescribing and secondary care data, to evaluate whether a pilot of an NHS funded pharmacy STTT service had an impact on antibiotic provision, patient safety and GP consultation rates. 11 No safety concerns were identified and findings suggested that STTT can be part of a collaborative approach aiming to re-balance uncomplicated sore throat management from general practice to community pharmacies whilst continuing to promote antibiotic stewardship.
The global response to infectious diseases has seen a renewed interest in the use of community engagement to support research and relief efforts. From a perspective rooted in the social sciences, the concept of vulnerability offers an especially useful analytical frame for pursuing community engagement in a variety of contexts. However, few have closely examined the concept of vulnerability in community engagement efforts, leading to a need to better understand the various theories that underline the connections between the two. This literature review searched four databases (covering a total of 537 papers), resulting in 15 studies that analyze community engagement using a framing of vulnerability, broadly defined, in the context of an infectious disease, prioritizing historical and structural context and the many ways of constituting communities.
The following databases were included to identify relevant studies published up to April 2020, when the search was carried out: PubMed, MEDLINE, Web of Science, and PsychInfo. The search terms include three concepts: (1) community engagement and other iterations thereof; in the context of (2) infectious diseases or epidemics; and using (3) concepts from social science to search specifically for those studies which take up the concepts of vulnerability, resilience, power, etc. • Community engagement, community engaged, public engagement, citizen engagement, community participation, public participation • Infectious disease*, communicable disease*, endemic, epidemic, pandemic, outbreak, preparedness • Vulnerable, vulnerability, resilience, resilient, sociocultural, sociocultural, political, economic, power, empower*, marginalized, marginalized, marginalization, marginalization, risk perception, qualitative.
Eligibility criteria All types of research studies were included, with a special focus on qualitative methods and observations or other social science studies on community engagement. The following eligibility (inclusion/exclusion) Fig. 1. Summary of minimum standards for community engagement  (UNICEF et al. , 2020, 12). criteria were selected for: • Inclusion: Primary scientific studies on original data (e. g. , no reviews or "opinion" papers); written in English; community engagement is the focus or method of published in peer-reviewed journals; full text available; only human studies • Exclusion: type of publication  (reviews, commentaries, opinions) ; not about infectious diseases; does not ground community engagement's concepts in a social science field; gray literature; not written in English; no full text available.
Procedure After running the search in the designated databases and duplicates removed from the results, two reviewers (JO and MD) independently screened titles and abstracts using Rayyan software  (Ouzzani et al. , 2016). Disagreements were discussed among the two reviewers and if consensus could be reached, a third researcher (JP) was consulted. The selection process was guided by the PRISMA checklist  (Moher et al. , 2009) , as illustrated in Fig. 2A  total of 537 results were found in all databases. When duplicates were removed the records were screened for eligibility, after which 30 full text articles were assessed, resulting in a final total of 15 articles. Analysis was carried out using thematic coding with MAXQDA software. A code book of themes and subthemes was developed by the first author (JO) after the initial screening of results.
Acknowledgements We would like to thank all the participants who took part in this study for their time and rich feedback, and all the doctors:  Stéphane Cattan ,  Luc Ceugnart ,  Anne Gandon ,  Victoire Lassaux ,  Samira Makhloufi ,  David Pasquier ,  Anne Ploquin , and  Thibault Voron. We would also like to thank all the researchers for their precious comments:  Eva Andreotti ,  Anne-Sophie Baudry ,  Anne Brédart ,  Laura Caton ,  Emilie Constant ,  Charlotte Dassonneville ,  Christelle Duprez ,  Cécile Flahault ,  Michèle Koleck ,  Yves Libert ,  Isabelle Milhabet ,  Marie Préau ,  Aurélie Untas ,  Clémence Willem , and  Emmanuelle Zech. Special thanks to  Mieczyslaw Cholewa ,  Fabien Delfosse ,  Michel Destrebecq ,  Eléonore Dumas ,  Marie-France Ghesquière  and  Manuelle Thebault. We also want to thank the  Centre Oscar Lambret  for their kind help, especially  Nicolas Penel  and  Sophie Costa.
A. Aim This study demonstrates an in-home calibration and classification procedure embedded into a rehabilitation video game for young people with CP. The procedure allows for: a. Home setup: training data are collected and processed with minimal adult involvement within the first minutes of gameplay. b. Therapeutic practice: Real-time gesture recognition identifies therapeutic movements for game feedback and control. c. Clinical insight: Features used for classification are associated with neuromotor signs of CP. The article is organized as follows. Section II A. details the software and hardware components of the system. Section II B. explains the home-based calibration and classification procedure. Section II C. contains information on 19 participants who completed the one-month intervention.
A. Requirements for Use The system requires hardware: laptop (typically used machine specifications: 2. 67GHz CPU, Intel Core i5-560M, Intel HD Graphics, 4GB RAM), electromyography (EMG) and inertial sensor (Myo Armband, sampling at 200 Hz and 50 Hz respectively  [13] ) and software: adapted commercial video game (Dashy Square) and custom controller to interpret movements and command the game (MATLAB 2017b). Briefly, the objective of the game is to avoid all obstacles while the character progresses across the level. In the original commercial version, players tapped the screen at the correct moment to avoid obstacles. For this work, the tap command was replaced with the gesture controller. Level difficulty increased by requiring faster and more precise timing. Example gameplay can be seen at the link: here.
Complete description of the commercial game adaptation are in an associated publication  [12]. Before playing, participants were instructed of the game objective and coached by their therapist on how to perform the therapeutic gesture. To accommodate smaller arms, the original 8-channel device was cut to 4 or 6 channels and the software adjusts accordingly (see code on: GitHub). This reduction does not introduce any bias since the collected signals are statistically independent of each other, whether there are 4, 6 or 8 channels  [14].
In development, this was the largest window length consistently undetectable by participants while playing  [12]. Window lengths between 50-300ms with overlap at 25, 50 and 75% were tested. No overlap value showed significant differences in classification performance and window lengths less than 150ms showed slightly lower performance  [12]. Features described in the next step are calculated for each of these bins. b) Feature selection: The most important features, derived from the EMG and inertial data collected with the Myo Armband, are selected via a 200 bootstrap-aggregated (bagged) random forest decision tree algorithm (MATLAB Treebagger  [15] ). Preliminary tests were made using 5-15 features and showed that after ten features, classification performance improvements were marginal. This variable is adjustable in the software.
We have therefore also included features expected to be sensitive to two signs of CP. Formulas for all features can be found in the Appendix and on GitHub. Features are put into three groups to facilitate clinical interpretation. 2 RA group -we considered features sensitive to uncommon Relative muscle Activities i. e. weakness, spasms, increased flexor tone, and impaired co-contraction  [7]. The domain specific features in this group are: Co-contraction index (CCI), Scaled co-contraction index (SCCI), Mean absolute difference normalized (MADN), Scaled mean absolute value (SMAV)  [21]. 3 MV group -we considered features related to Movement Variability which may be associated with impaired selective motor control and compensatory movements  [7]. To capture muscle activity precision around the forearm we employ directional statistics  [22].
The model is then used to predict gestures during gameplay. During preliminary design, alternative classifiers were tested (linear discriminant analysis, nearest neighbor, ensembles and naïve Bayes) details described in Section II -Analysis. 2) Part 2 -Processed During Game Play: After game values are initialized and the on-line classifier is prepared, participants can practice the targeted therapeutic gesture within the video game. EMG and inertial data from the Myo Armband are processed continuously as follows: a) Buffering & pre-processing: Absolute values of the raw 8-bit EMG data from the Myo Armband are normalized to a 0-1 scale. For inertial data, the squared sum is calculated for the 3-axis accelerometer and gyroscopic data respectively. These are then buffered into a 200ms bin. This window length was determined during design session testing with youth with CP  [7].
If the current activities of the extensor and flexor channels are respectively above and below these thresholds, the user is considered to be attempting a gesture and data will be processed further to dictate game actions. If the thresholds are not met, processing stops until a new 200ms bin of data has buffered. c) Dictate game actions: When the threshold is exceeded, game actions occur, i. e. players move, points are awarded, feedback is presented. Game actions are dictated by movement quality. Movement quality variables include: extensor-flexor co-contraction ratio, forearm angular acceleration and the predicted gesture. Based on these evaluations, commands are passed to the game and points are awarded. d) Predict current gesture: To obtain the predicted gesture for the current 200ms bin of data, first the top 10 features are calculated (as established in Part 1 -processing before game play).
D. Analysis Descriptive summaries of participant characteristics are provided first. Gestures were verified by visually labelling videos collected weekly by the researcher during a game play session. A visual signal emitted from the game software synchronized video, EMG and inertial data. Initiation and termination of each gesture were labeled manually (True Labels) as a ground truth. The number of True Label samples are provided for each participant in Table  I. 1) First Aim, Home Setup: Two measures were used to show that training data could be collected, processed and used within the first minutes of gameplay. First, we use the agreement (raw and Gwet's chance-adjusted index agreement coefficient  [28] ,  [29] ) between the assumed gesture, prompted during calibration, and the true gesture, verified by video.
A misclassified, false negative observation of the target gesture frustrates and reduces the user's confidence in the system. Whereas, in the situation of a false positive, game play continues and there is less risk for negative effect. Accordingly, three measures (F1-Score, MCC, Sensitivity) are prioritized. Performance across participants for the whole dataset is presented for randomly partitioned off-line classification using the SVM procedure described above. To validate the use of SVM, multiple classifiers on a subsample of five participants were evaluated. Five participants were chosen to represent the distribution in functional ability and performance. Two were at MACS level II and three were younger than 12 years old. Tested classifiers include decision trees, linear discriminant analysis, nearest neighbor, ensembles and naïve Bayes  [31].
The top two performing classifiers based on this subset are presented here. These are the SVM and the random subspace ensemble classification of k-nearest neighbor learners (ENS) (MATLAB fitensemble)  [23]. Full model specification are available on GitHub. Reported also are weekly changes in the true positive and true negative rate (AUC) during home-based classification. AUC was computed across classes and then averaged across all participants. Here only calibration-game data up to the current week were used to train the model and tested against actual game-play data. To further evaluate the approach, performance of the SVM models with personalized features was compared to a model derived from all participants data combined (e. g. an 'All-Users' model). 3) Third Aim, Clinical Insight: First, we assess the value of feature groups expected to be sensitive to signs of CP (uncommon relative muscle activities, RA-group, and high movement variability, MV-group) as compared to traditional channel-independent features (CI-group).
ACKNOWLEDGMENT The authors are greatly appreciative of the time and input of the clients and families who helped us build and test this system. They also wish to acknowledge the occupational therapists and research administration staff who supported this project at  Holland Bloorview Kids Rehabilitation Hospital and Fondation Ellen Poidatz. Special thanks to the head of the DISI, and to the head of the UFR ST, of  University of Evry  for lending computers. And to the developer of the game, KasSanity Inc. They also wish to acknowledge the data processing and administrative assistance of the Research Manager.
Exclusion criteria. We excluded reviews that did not include any randomised controlled trials, did not report a pooled quantitative analysis on a health behaviour change outcome, or that focused only on theory-based process measures (e. g. , intention, risk perceptions) or only on psychological or physical health outcomes (e. g. , well-being, weight). Screening. Two authors (GtH, ET) used Covidence to independently screen titles and abstracts, then full-text, resolving conflicts with a third reviewer (JP) when needed. Data extraction. Two authors (LBD and AD) extracted the author, year, target population, behaviour (s), setting, and description of behaviour change interventions from included articles. They then independently double-coded whether each included review had any mention of the readiness of the intervention being synthesised for moving to implementation research and practice (coding: yes, no, unclear) and whether the review specifically extracted data on implementation readiness (yes, no, n/a, unclear).
Effectiveness has not improved over time  (Ivers et al. , 2014a). Reasons underpinning this variability and lack of progress are unclear, in part due to poor description of the A&F interventions, and because A&F has been designed and delivered in various ways, often lacking explicit rationale or theory  (Colquhoun et al. , 2017). The application of theory and frameworks developed in psychology and applied in health psychology in evidence syntheses has enabled implementation researchers to identify active ingredients of interventions associated with increased effectiveness. Control Theory  (Carver & Scheier, 1982)  is a mechanism through which A&F might operate and  Gardner et al. (2010)  re-analysed data from an earlier Cochrane review of A&F interventions to change professional practice, coding for the presence of these theory-consistent techniques.
Target audience, pre and post-intervention evaluation Using the results of the mapping, we targeted departments with the same patterns: 1) high level administration days of carbapenems and 2) high proportion of carbapenem UAP. We conducted a retrospective audit of carbapenem prescriptions in the selected departments from May to October 2015 (pre-intervention evaluation) and from February to July 2017 (post-intervention evaluation). Inclusion of several infectious events with carbapenem prescription for the same patient was possible. All data were extracted from EMR and CPOE system. Benjamin Viala reviewed each EMR and determined compliance of prescriptions with local guidelines (see Supplementary Figure  1 ), French guidelines for the management of urinary tract infections  [13]  and recommendations of the Fourth European Conference on Infections in Leukaemia (ECIL-4) for neutropenic patients  [14].
Discussion We used data from the clinical information system to further evaluate prescribing behaviours according to each class of antibiotics and interactions between prescribers and IDS. This mapping allowed a focused intervention using self-supervision, which was targeted on wards with a high rate of carbapenem prescriptions without advice of IDS. We showed that this intervention was feasible using very few resources and led to improvement of conformity of prescription to guidelines. Results also allowed us to make real-time modifications of our local recommendations. Our method allowed us to highlight patterns of interaction between physicians and IDS for antimicrobial prescription. For pattern I, characterised by high volume of prescription and low proportion of supervision rather equally distributed in all departments, relevant interventions may be implementation of guidelines applicable to all departments.
Statistical Analyses Assuming no difference between study groups, 70 evaluable infants per group would be required to achieve 80% power to show equivalence between groups of the IGSQ sum score at Week 4 (a ¼ 0. 05; equivalence margins based on 0. 5 standard deviation [SD] that was assumed to be 4. 1). Anticipating a non-evaluability rate of $20% resulted in a total number of 180 infants to be randomized. A pre-specified interim analysis including a semi-blinded comparison of the study groups was performed to evaluate GI tolerance and safety data of the first 73 subjects. Results were reviewed by the independent Data Monitoring Committee who recommended the continuation of the trial without modification. The primary analysis was based on the Per-Protocol (PP) population, as defined before database lock.
Equivalence between groups was examined by analyzing whether the two-sided 90% confidence interval (CI) of the difference in the mean IGSQ sum score at Week 4 laid within the predefined equivalence margins (a ¼ 0. 05). Data were analyzed using a linear Mixed Model Repeated Measurement (MMRM) approach on the post-baseline IGSQ scores with treatment, time (categorical) and interaction between treatment and time as fixed effects, IGSQ score as adjustment covariate and center as a random effect. Sensitivity analyses included the analysis of the all-subjects-randomized population, assessing the missing at random assumption and adjustment for potential covariates. A prespecified interaction test was performed to analyze the impact of the baseline IGSQ score on the intervention effect (predefined cut-off P-value < 0.
1 used to keep interaction in the model), followed by post-hoc subgroup analyses using a similar MMRM model as described above. Stool frequency and the Adapted Vandenplas Scale scores were compared between groups using a Mann-Whitney test. A CochranÀMantelÀHaenszel test was used to compare groups for the distribution over the different stool consistency categories. All statistical analyses were performed using SAS Life Science Analytics Framework version 4. 7. 3 (SAS Institute Inc, Cary, NC, USA).
Acknowledgments: We are grateful to the families who participated in this study, as well as to the participating investigators and site staff members in the  LeopARd Study Group  who conducted the research. We would also like to acknowledge the contribution of the LeopARd study team. Thank you to  Prof. Dr med. S. Koletzko ,  Kinderpoliklinik Munich ,  Germany ;  Dr M. van Wijk ,  Academic Medical Centre Amsterdam ,  The Netherlands  and  Prof. T. Stijnen ,  Leiden University Medical Center, The Netherlands  for their role in the Data Monitoring Committee of this study.
Material and methods DaWCo is an open inception cohort study of all individuals who first entered the Danish workforce during the years 1995-2009 and were 15 to 30 years old at entry  (Svane-Petersen et al. , 2019). DaWCo was constructed using nationwide Danish registers on employment, health, demographic-and socioeconomic factors to examine effects of working conditions on health. Working conditions are measured repeatedly throughout the work life and health endpoints are based on clinical diagnosis. Workforce entry was defined as the first year with employment as the main source of income (n=979,257). We excluded individuals with missing data on gender and ethnicity (n=5,176), and individuals who died (n=71), emigrated (n=13,087), or received disability pension (n=361) in their year of entry, leaving 960,562 individuals in the cohort.