dataesr/scientific-paragraphs-categorization huggingface dataset:
In case the MCAR is violated, we will further examine the MAR pattern by comparing the outcome in the variable with and without missing values. Thus, we will extract data on: 24 months and over the duration of the trial 2. Overall and symptomatic venous thromboembolism (DVT and/or PE): DVT events must be diagnosed using an objective diagnostic test such as: venography, or compression ultrasound. PE events have to be diagnosed using an objective diagnostic test such as: pulmonary perfusion/ventilation scans, CT, pulmonary angiography or autopsy 3. Health-related quality of life: has to be measured using a validated tool 4. Major bleeding: we will accept the authors' definitions of major bleeding 5. Minor bleeding: we will accept the authors' definitions of minor bleeding 6. Thrombocytopaenia.
Data analysis We plan to use SAS V. 9. 3 (Cary, North Carolina, USA) or a newer version of this software to analyse data. All analyses will follow the ITT principle with all patients analysed according to the arm to which they were randomised. A consistency check of the raw data from all trials will be performed. In addition, to ensure that the data provided correspond to the reported results, we will reproduce and recalculate the primary outcomes of the trials and several randomly chosen baseline data. For the variable that has missing data, we will use a multiple imputation method to impute the missing data. We will use the imputed data sets as the main study datasets for the statistical analyses. We will summarise the data in mean or median, depending on the distribution, for the continuous outcomes (eg, quality of life) together with SD or IQR.
All data will be summarised in a GRADE evidence profile. In addition, we will perform several prespecified sensitivity analyses to investigate differences in pooled effect estimates related to conduct or methods. We will also perform subgroup analyses to investigate differences in pooled effect estimates related to different patient subgroups. We will test whether there is a differential intervention effect among the various subgroups with an interaction-test, which is preferred to separate subgroup group-specific analyses. 35 36 ubgroup analyses will be performed for the following variables: 1. Underlying diagnosis of included patients (type and stage of cancer): We hypothesise that patients with earlier stage cancer have greater benefit than those with later stage cancer. We also hypothesise, based on our prior analysis, that patients with lung or pancreatic cancer experience a greater benefit from anticoagulant therapy.
13. Concomitant treatment (ie, chemotherapy compared with no chemotherapy): In patients treated with chemotherapy we anticipate larger effects of heparin. Sensitivity analyses: We have prespecified the following sensitivity analyses: 1. Compare the results of a study-level meta-analysis including studies for which we will not receive original datasets with the main IPDMA results. We will then explore if the study-level data differ after excluding the studies for which we do not receive original data sets. 2. Higher risk of bias in the original study may be associated with a greater effect. We will conduct sensitivity analysis by comparing results of trials judged at high risk of bias compared to those judged at low risk of bias (those that are judged as not suffering from any of the methodological limitations listed in the risk of bias assessment above).
To adjust for overfitting, regression coefficients will be shrinked as needed. The regression coefficients will be used to calculate individual annual risks and corresponding 95% CIs. Bootstrapping procedures will be performed for internal validation of the performance of the new rule. In parallel, we will perform an external validation of the Khorana score  37  and extended Khorana score,  38  using the individual data of patients not receiving LMWH. We will recalculate individual risk for patients at baseline (expected, E) using the Korana score and the extended model including D-dimer and soluble P-selectin concentration. We will then assess the incidence of VTE (observed, O), for each score on the Khorana or extended Khorana score. We will perform net classification analysis and offset regression to verify the need for recalibration, and proceed to recalibrate if necessary.
Acknowledgements This study was supported by the  Canadian Institutes of Health Research  (http://www. cihr-irsc. gc. ca), grant  KRS 126594 , and registered in Prospero ( CRD42013003526 ). Contributors HS conceived of the study and obtained funding. The concept and design of this study were generated primarily by HS, EAA, MB, MC, JB and GL. A Knowledge Synthesis grant proposal submitted by HS, EAA, MB, MB, JB, JB, MC, GG, Lavis, GL and NS to The  Canadian Institutes for Health Research  funded this project. The study protocol was primarily drafted by HS, EAA, MCr, JB and GG, however, all the authors provided feedback and offered insight regarding potential revisions. MV was responsible for providing administrative support.
Sample size determination To study the impact at 3 months of the 6-day standardized short thermal intervention on pain-related functional impairment measured using QUICK DASH scale (disability/symptom section), the sample size estimation is based on a comparison between both arms for a twosided type-I error of 5 and 90% statistical power. According to the literature  [26] , it seems relevant to consider a standard deviation at 18 and a baseline value equaling 48 (/100) for the QUICK DASH scale. Considering (i) an assumed 25% improvement (-12 points) in favor of the 6-day standardized short thermal group, (ii) a possible Hawthorne effect (in the "delayed treatment" group), and (iii) a lost-to-follow-up rate of 20%, it is planned to include 75 patients per randomization group. Data will be entered in real time using the online clinical research data management tool REDcap (Research Electronic Data Capture).
Probing was used to explore any relevant issues about which the interviewee had particular insights or experience. Interviews were approximately 1 hour long, conducted in Russian at the workplace of the interviewees, with no one else present. Interviews were recorded with consent, and then transcribed verbatim. 15 Interviewees were not reimbursed for participating. A process evaluation was also undertaken, consisting of a written questionnaire completed periodically during the study by several groups of health services personnel. This served to triangulate data, capture changes over time and encourage staff to engage in their own analysis of the processes involved in LPA implementation and policy change. Content analysis was conducted simultaneously with data collection. 15 Coding categories were generated initially from the framework, using the basic components of the model as a starting point for category identification, then from the data in an iterative manner, emerging from participants' accounts of their experiences.
TB diagnostic implementation: qualitative study 183 ing categories describing LPA implementation in the Arkhangelsk region. Qualitative data analysis software Open Code 3. 4 was used to organise and code the interviews. 16 oding was performed in Russian by the PI and the codes were then translated into English. The coding and results were discussed with the non-Russian-speaking qualitative researcher (KB), who works for an organisation focused on TB and lung disease. The PI does not work in the management of TB, which helped to minimise potential bias and ensure that data collection and analysis could allow new knowledge to emerge. Categories and themes were organised to provide theoretical coherence regarding the participants' experiences of LPA piloting, implementation and uptake, barriers and facilitating factors, and suggestions for the future of LPA expansion and the piloting of other diagnostics or treatment algorithms.
Data can be analyzed to support diagnostic decisions and support the clinicians' treatment decisions, but their collection is mainly dominated by security of communications and issues related to data privacy. Data can be collected by various sources, but the quality of analysis, is affected by the quality of the source as well as the consistency of measurements. Harmonising cohorts, data sources and datasets will improve the quality of the decision outcome. Finally, predictions can be performed using predictive computational models, having parameters which must be tuned to avoid limitations such as suboptimal patient stratification.
The participants were instructed to analyze the sample in the provided geometry and using gamma-ray spectrometry and to report activity concentrations of 60 Co. The elemental analysis of the raw material of the originally contaminated samples (ILC_A) was conducted at different partnerlaboratories 1  using X-ray fluorescence (XRF) (at the IJS and the CIEMAT) and Proton Induced X-ray Emission (PIXE) (at the IST/ CTN) techniques, while carbon content was determined by elemental combustion analysis (at the CIEMAT). The results from different partner-laboratories showed a satisfactory agreement amongst them. CIEMAT's composition data were adopted as most complete. Regarding the spiked samples (ILC_B) the composition of the raw material was provided from the manufacturer based on analysis using Glow Discharge Optical Emission Spectroscopy (GDOS).
Acknowledgements This work was supported by the  EMRP joint research project  ' Ionising radiation metrology for the metallurgical industry' (Me-troMetal ) which has received funding from the  European Union  on the basis of Decision No  912/2009/EC. The EMRP is jointly funded by the EMRP participating countries within EURAMET and the  European Union. The authors are grateful to all contributors to this work at the participants' laboratories and in particular to  Carlos Oliveira ,  Pierluigi Carconi ,  Teemu Siiskonen ,  Franz-Josef Maringer  as well as to  Maria Sahagia  and  Thierry Branger  who in addition reviewed thoroughly and provided valuable suggestions on the manuscript. The authors gratefully acknowledge the work of  Belén Gómez-Mancebo  and  Fernando Borlaf , from  Unidad de Espectroscopia from CIEMAT , who performed the steel composition analysis.
Shown in Figure  4  is the circuit design, built on OPA4227 operational amplifiers. Each channel was buffered with noninverting differential inputs. Next, an amplifying stage with adjustable gain and simple low-pass filtering was added. A cut-off frequency of 30 Hz was chosen, because, in terms of our experiments, slow changes in body balance were of most interest. Additional filtering was implemented by a Sallen-Key low-pass filter with the same cut-off frequency. The circuit was powered with a differential supply of ±10 V DC , which required approximately 80 mA of current. Output signals were acquired using a National Instruments USB-6008 data acquisition card, connected to a laptop. Naturally, in addition to the embedding method and features of the sensor itself, the sensitivity and dynamics of the sensor also depend on the amplification of its output signal.
Study Conduct At enrollment, the infants' and their mothers' demographic data were recorded. Birth anthropometric measurements, 1-, 5-, and 10-min APGAR scores, and medical history of infants were recorded. Eligible infants were randomized between formula groups. Parents/caregivers received 3-d diaries for recording of formula intake and digestive tolerance (stool characteristics, occurrence of flatulence, and colic) for the 3 d preceding each visit to the study center. All infants visited the study site at 1 mo (±3 d), 2 mo (±3 d), 3 mo (±3 d), 6 mo (±7 d), 9 mo (±7 d), and 12 mo (±7 d) of age. At each visit, the investigator examined the infants, assessed any adverse events (AEs), took their anthropometric measurements, and reviewed their medical records and 3-d diaries. Saliva and fecal samples were collected from a subset of infants at 3 and 6 mo.
For normally distributed continuous variables (i. e. , weight, length, head circumference, and BMI), the following linear mixed models were constructed to compare Test and Control; and Test and breastfed groups. Baseline measures of corresponding dependent variables, gender, and delivery mode were included in the model: dependent variable (e. g. , weight) = baseline + group*visit + gender + delivery + random effects (within-subject variability). If logarithmic transformation was needed to ensure normal distribution of data, the logtransformed data were used in the analysis. A starter formula promotes gut health.
ACKNOWLEDGMENTS The authors thank  Claire Hubert  for her commitment, and the diverse study teams for their major contributions to this study. They also acknowledge  Antonio de Castro  for the statistical analysis,  Jian Yan  for his scientific expertise and  Annemarie Beekman  for study co-ordination and follow-up. Finally, the authors are grateful to the families and caregivers who consented to their infants' participation in this study.
Ophtalmologic and Orthoptic Examination All strabismic children underwent ophthalmologic and orthoptic examination to evaluate their visual function. The Table  1  shows clinical data of each child before and after strabismus surgery. The visual acuity was measured for each eye separately at far distance (5 m) with the Monoyer chart (an optometric chart containing 10 rows of letters, each row corresponding to 1/10 visual acuity). The type and the angle of strabismus (i. e. , the manifest deviation of one eye) was measured at near (33 cm) and far distance (5 m) using a base-in and a base-out prism with a Berens prism bar. Stereoacuity threshold based on disparity detection was evaluated with the TNO random dot test for stereoscopic depth discrimination. The monocular visual acuity varied between 20/63 and 20/20.
Data Processing To quantify the effect of visual sensorial conditions on the postural performance, several postural parameters were analyzed: the surface area (mm 2 ), the length (mm), and the mean speed (mm/s) of the CoP. Note that all these postural parameters are used to evaluate postural capabilities in humans and a decrease of such parameters suggested an improvement in postural stability  A. F. P. , 1984; Gagey et al. , 1993. The surface area and the length of the CoP measures the its spatial variability. The surface of the CoP corresponds to an ellipse with 90% of CoP excursions. The length of CoP is the path of the CoP. These two postural parameters are uncorrelated; indeed, the inner surface of the same length can be different  (Vuillerme et al. , 2008). The mean speed of the CoP represents a good index of the amount of neuro-muscular activity required to regulate postural control  (Geurts et al.
Statistical Analysis An ANOVA was firstly performed for comparing the two different groups of children with convergent and divergent strabismus but it failed to show any statistical difference between the two groups of children. Consequently postural data reported below will be showed for children with convergent and divergent strabismus together. For all children, analysis of variance ANOVA with repeated measures was used to compare postural data in the three different visual conditions and in the two postural conditions (with and without foam) and the Greenhouse-Geisser correction was applied. The post-hoc Bonferroni was also performed. For the explore the eventual surgery effect, a similar ANOVA was with 3×2 × 2 (vision, foam, and surgery) factorial within-subject analysis was applied. The effect of a factor was considered as significant when the p-value was below 0.
Procedure All the eligible women were asked for a 30-minute interview. The first part of the interview was used 1/ to check selection criteria, 2/ to collect socio-demographic and anamnestic data and, 3/ to create an empathetic and caring atmosphere liable to establish a working alliance. Then the women were audiotaped for 5 min of spontaneous speech. They were asked not to talk about their pregnancy and were excluded in case of explicit reference to it. The instructions were as follows (translated into English here to provide information on the content): "Take roughly 5 min to talk about yourself. For instance, you could talk about the value you attach to your dreams, about their possible meaning and about their importance in the morning. How much room do you give them in your everyday life? You could also talk about their strangeness and their impact in your life, for example, the feeling of "déjà vu", or the feeling of being in a waking dream.
Acknowledgements Authors thank all the participants for agreeing to take part in the study, and  Marion Bonnard ,  Camélia Boukhris ,  Florence Brisson ,  Elsa Brune ,  Lucien Coudrin ,  Anne-Sophie Courtois ,  Delphine De Guibert ,  Sébastien Douabin ,  Marc Fedele ,  Gwenaëlle Felard ,  Fanny Gohars ,  Samantha Guimaron ,  Morgane Hamouric ,  Aurore Lamberet ,  Laurence Lebas ,  Raphaël Leroux ,  Sébastien Le Texier ,  Andre Madrid ,  Edmond Oriol ,  Louise Perrier ,  Ouriel Rosenblum ,  Hélène Spriet ,  Philippe Taillet ,  Xavier Taillet  for assessing the videos.
Environmental exposure "negative and positive" The high-traffic roads database and topographic database information was provided from the National Geographic Institute. Proximity to major roads with high traffic was determined as the proportion of residential building located within a specified distance of the hightraffic roads (minimum 70 km/h) in each census block  [38]. More precisely, circles with radii of 150 m were created around each road segment according to the literature  [38, 39]. The measure of proximity was determined by select residential buildings located inside each circle which will be considered exposed to the hightraffic road and the measure for census block was obtained by dividing the total number of exposed residential building by the total building of the census block.
Proximity to green space The green space databases were providing by the Corine Land Cover website  [40]. The definition of green space included natural area (e. g. parks, forest, garden…); to not include pesticide in our green space definition, agricultural areas have not been taken into account. Two green space indicators have been created. The first indicator is defined by the proportion of the geographic area occupied by green spaces within the total area of a census block  [41, 42]. The second indicator is defined according to the World Health Organization (WHO) guidelines which recommends at least 10 m 2 of green space per habitant  [43, 44].
Quantification of mortality impacts Neonatal and infant mortality are defined as the number of babies who died during their first month and their first year of life per 1000 live births that occurred during this time period, respectively. Cases were collected from death certificates in the city hall of each municipality in the Nice MA. Using the parental postal addresses, cases were geocoded to their census block of residence using ArcGIS version 9. 1 (ESRI, Redlands, CA). The French data protection authority (CNIL) approved the study (number: 911149v1). Figure  2  illustrates the spatial distribution of number of cases of infant and neonatal deaths by tertiles at the census blocks level of Nice MA.
MCA analysis is one of the most popular dimensionality reduction methods dealing with categorical variables. The MCA method permits to take variable collinearity into account, thereby avoiding redundant information. Hierarchical clustering is frequently used after MCA  [45]  in data mining to create meaningful categories. A distance between elements (usually the Euclidian distance) and a distance between categories are defined. Thanks to the Ward's distance, this algorithm allows to obtain homogeneous categories in their composition and heterogeneous between them (i. e. with maximum between-categories inertia). These analyses were made with FactoMineR, a R package dedicated to multivariate Exploratory Data Analysis  [46]. Secondly, we performed a standard Poisson regression model for count data after checking the assumption of overdispersion.
Strengths and limitations of this study ▪ Some software programs were built to promote frequent breaks at work for visual display unit users but only very few tools were evaluated with a high level of proof. ▪ The novelty of the I-Preventive strategy is its implementation of frequent short daily active breaks promoted by a software program, which can only be achieved within a preventionoriented organisational culture. ▪ The I-Preventive software program is particularly effective in the short term due to its individualised and personalised form, allowing workers to focus on areas of their choice by offering exercises that are easily performed in the workplace. ▪ Despite this digital tool being easy to use, adherence decreased over time, and its favourable impact was not maintained over the long term.
Researchers were composed of two occupational physicians (CL and GG). GG was in charge of selection and inclusion. CL was in charge of the data treatment under blind conditions to the treatment group. This study was conducted from June to October 2014. Measurements were made with repeated data requests. All employees corresponding to the inclusion criteria received a summary of the study, which was considered a non-opposition form. This nonpharmacological trial was designed in accordance with the Consolidated Standards of Reporting Trials statement (CONSORT). 17.
Intervention Workers in the intervention group received an activation code to download the software program (I-Preventive, http://www. i-prentive. com) on their VDU. I-Preventive promoted active breaks via an individualised computer application. It allowed workers to focus on body regions of their choice, offering high flexibility. After selecting the body part, I-Preventive asked about symptoms and suggested appropriate exercises. Workers could follow the suggested personalised exercises or use the random mode. At regular intervals, a visual signal on the screen prompted VDU workers to take active breaks. The visual signal described short individualised exercises of no more than 1½ min. The exercises were easy to perform and most could be carried out while seated without needing specific equipment (figure  1 ).
Statistical analysis was performed using Stata software, V. 13 (StataCorp, College Station, Texas, USA). The tests were two-sided, with a type I error set at α=0. 05. Baseline characteristics were presented as mean±SE or median (IQR) according to statistical distribution for continuous data, and the number of workers and associated percentages for categorical parameters. Comparisons among independent groups (workers accessing I-Preventive or not) were performed by Student's t-test, or by the Mann-Whitney U test if t-test conditions could not be respected (homoscedasticity analysed by the Fisher-Snedecor distribution). Comparisons between qualitative parameters of the independent groups were performed by χ 2 test or, if necessary, Fisher's exact test. For the evolution of the longitudinal Nordic study (and visual discomfort score), mixed models (the linear models for the Nordic score considered a quantitative or generalised linear for the proportion of VDU workers with Nordic scores above a given threshold, in line with the literature  18  ) were proposed in order to take into account the difference between and within individual variability (random effect) while studying the fixed group effects, time and interaction.
The normality of residuals was assessed and gender, age, site, and original pain or discomfort were studied in previous models as covariates (fixed effects). Workers' frequency of I-Preventive use (before or after holidays) was also recorded, particularly in the context of a specific intragroup analysis of workers who had access to the I-Preventive software. Finally, this was complemented by analyses that were situation-matched to the parameters collected at only two time points (M0 and M4 for HAD, with the usual tests applied: Student's paired t-test or the Wilcoxon signed-rank test for quantitative parameters and the Stuart-Maxwell test for categorical variables.