dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, because Down syndrome is a well-established risk factor for ALL, and is also associated with a range of other morbidities, the 22 surviving case children with Down syndrome (no first-choice control had Down syndrome) and their matched controls were excluded from the analysis. For the 1082 cases and 2018 controls included in this report, linked healthcare data were available on all cancer registrations and deaths up to March 2020, inpatient HES from April 1997 to March 2019, and outpatient HES from April 2003 to March 2019. Data were analysed using non-parametric time-to-event analyses and exact methods to estimate hospital visit hazard rates and risks associated with specific clinical specialties (two or more outpatient visits, or at least one hospital admission). Follow-up began five years post-diagnosis and ended either at the date of death, 25 years post-diagnosis, or end date for hospital data.
Missing information within the HES datasets is also an issue; diagnostic data, for example, is largely absent from outpatient HES. Furthermore, although steps to mitigate surveillance bias were taken by requiring at least two face-to-face visits to a given specialty, we cannot rule out the possibility that childhood cancer survivors were more likely to be referred to secondary care than their peers. Adding to a large excess risk for death and cancer, survivors of childhood ALL also experience excess outpatient and inpatient activity across their teenage and young adult years. Involving most clinical specialties, the observed effects are striking; showing no signs of diminishing over the first 25 years of follow-up. The main exception is the comparatively low midwifery contact seen among female ALL survivors.
MATERIAL AND METHODS A cohort of all patients undergoing endoscopic vocal fold lateralization from 2016 to 2018 was retrospectively reviewed at a tertiary referral laryngology center. Informed consent for data collection in medical records was obtained. All procedures performed in the study were in accordance with the ethical standards of the institution and with the 1964 Helsinki declaration and its later amendments. Exclusion criteria were any preoperative associated laryngeal procedure other than lateralization or any history of laryngeal surgery for vocal fold paralysis, patients under the age of 18, and death before postoperative follow-up secondary to a well-identified cause independent of surgery. Time elapsed from onset of vocal fold paralysis to the lateralization procedure was at least one year.
Construction of the rst version of HIBOOT The construction of the app (Step 2) is presented in supplementary material 5 and 6. The steering committee and the French Society of Rheumatology decided that no personal information would be collected. Consequently, the app was constructed as a self-administered and self-management information app and was not registered as a medical device. Patient data were not to be communicated to rheumatologists or general practitioners. However, patients could communicate with their physicians about their data or app use during physical consultations or by Internet with screenshots. The app was free of charge and the chosen look was a friendly companion called HIBOOT (OWL in English) that interacted with the user. The owl evokes a watchful animal, attentive to safety, and its small size can make it a companion (HIBOOT features in supplementary material 5).
Acknowledgements. We thank all patients who participated the qualitative and quantitative studies; patients and rheumatologists who participated in the users' feedback and app improvements;  Mme Nathalie Robert  from the association Spondyl(O)action for users' feedback; Mahed Iqbal (Unknowns) and  Alexandre Galatioto  ( Unknowns ) for development contribution;  Damien Carnet  ( ANDAR ) for posting the quantitative study questionnaire;  Laura Smales  for reviewing the English version; and  Frédéric Lioté ,  Hôpital Lariboisière, Paris , who contributed to patient recruitment in Q2.
Data are n (%) unless otherwise indicated. Availability in rst version Availability in current version Key words in current version (no. ) Availability of general information¹ Availability of speci c information² Availability in rst version Availability in current version Key words in current version (no. ) Availability of general information¹ Availability of speci c information² Availability of data and materials The datasets used and/or analysed during the qualitative and quantitative studies are available from the corresponding author on reasonable request.
We excluded MPAL cases because they are frequently treated off-study and can receive hybrid therapies  [9]. ZNF384 rearrangements were detected by fluorescence in situ hybridization (FISH), reverse transcription polymerase chain reaction, and/or NGS as per individual study group criteria. Three groups identified 18 cases with ZNF384 rearrangements among patients with relapsed or refractory ALL, and these cases were excluded from the outcome analysis. We collated data on the clinical and biological characteristics of patients with ZNF384-rearrangements from each study group and analyzed their association with clinical outcomes. Overall survival (OS) was calculated from the time from diagnosis to death, while event-free survival (EFS) was defined as the time from diagnosis to induction failure, relapse, a second tumor, or death; time was censored at the date of last patient contact if no event occurred.
Relapse rate was defined as the time from diagnosis to relapse censoring at other events. The Kaplan-Meier method was used to estimate survival rates, and evaluation of the equality of the survivorship functions in different subgroups was performed using the two-sided log-rank test. Univariate Cox regression models were used to determine hazard ratios (HR). Other comparisons were performed using the χ 2 test or Fisher's exact test as appropriate. All probability (P) values were two-sided, and P values < 0. 05 were considered statistically significant. All analyses were performed using Intercooled Stata (Statacorp 2015 Stata Statistical Software Release 14; StataCorp, College Station, TX). Among the 218 patients with B-ALL and ZNF384 rearrangements, information on the partner gene was available for 193 cases with frequencies of 43% for EP300, located at position 22q13.
Data collection method The sociodemographic data gathered during the social survey carried out during or after the consultation (face-to-face and/or telephone) were extracted from hard copies of archived files for the year 2018. All the data concerning the child and the characteristics of his or her visit to the ED were collected from ED reports  [2]  computerized using Urqual Ò software. To identify the data on migrants' journeys, the child's hospitalization and/or consultation reports available and computerized on ORBIS Ò software were analyzed. In order to simplify the interpretation of data concerning pathologies, these were standardized using a simplified thesaurus from the International Classification of Diseases (ICD-10). Data from the 2018 annual report by the Observatoire R egional des Urgences Occitanie (Regional Observatory of EDs in Southern France) on ED activity in the Toulouse University Hospital were used to compare the data in the sample with data from the general pediatric population.
Data analysis methods The statistical analysis was performed by the Research Methods Support Unit (RMSU). For the qualitative variables, we present the results in the following format: Total number (N), absolute number (number of cases n for the category concerned), and relative number (percentage (% = n/ N for the category concerned). For quantitative variables, the results are presented in the following format: mean (m), standard deviation (SD), median [3], quartiles (Q1, Q3), and range. To compare the quantitative variables (age, duration of visit) with the values found in the ORU database of the same year, we compared a bilateral Student t-test with a theoretical value, after checking the conditions of application. To compare the quantitative variables (age, duration of visit) with the values found in the ORU database of the same year, we used a bilateral chi-square test after checking the application conditions, or Fisher's exact test if required.
Purpose This study investigates the impact of an intensive case management program on sick leave days, permanent work incapacity levels and treatment costs for severe vocational injuries set up by the French National Insurance Fund in five health insurance districts. Methods The method employed relies on a four-step matching procedure combining Coarsened Exact Matching and Propensity Score Matching, based on an original administrative dataset. Average Treatment effects on the Treated were estimated using a parametric model with a large set of covariates. Results After one-year follow-up, workers in the treatment group had higher sickness absence rates, with 22 extra days, and the program led to 2. 7 (95% CI 2. 3-3. 1) times more diagnoses of permanent work incapacity in the treatment group.
The program included home visits (at least once to enroll the beneficiary) and assistance with injury-related tasks (administrative, medical, social, professional). Both programs focused on severe accidents and had as objectives to support patients' rehabilitation and to reduce costs. A RCT was initially considered but deemed infeasible for both ethical and practical reasons. Thus, the evaluation resorted to non-experimental matching techniques. The empirical strategy relied on creating balanced groups using a four-step matching procedure combining Coarsened Exact Matching (CEM) and Propensity Score Matching (PSM), taking advantage of a rich dataset containing key variables influencing the probability of treatment and the outcome results, and enabling a thorough control for the initial severity of the injury faced by the worker.
Inclusion/Exclusion Criteria The data were extracted from the French National Health Insurance Database (SNIIRAM) and the linked national hospital discharge database (PMSI). The case managers' database, used for the selection of eligible patients, was linked to the national database to identify beneficiaries. All data cleaning and analyses were performed using R Statistical Software, version 3. 4. 1. Ethical approval for the program and for this study was obtained from CNAMTS (reference numbers: 254-5-2014 and 46-29-2016). The treated group was defined as all workers who enrolled by signing the letter of participation to the case management program and whose accident occurred in 2015. Accidents occurring in 2014 were not considered in the analysis because the program was still in its early days.
Exclusion criteria were defined as follows: injuries that could not be identified in the national database due to coding errors; patients with chronic low back pain-who were offered a significantly different version of the case management program-; relapses, as they would not be comparable to initial injuries, either because the relapse variable indicated so or because workers received disability pensions during the first month after the accident. After implementation of these exclusion criteria, the treated group on which matching was performed contained 269 observations (see Section "Matching and Balance of Samples"). A first definition of the control group entailed all vocational injuries from the same five districts and the same year (2015) in which the program was implemented. However, as many as 79 treated individuals could not be matched by a control observation, resulting in an unsatisfactory balance between treated and control groups (see Online Appendix).
Control Variables Following Caliendo and Kopeinig  [14] , the control variables were chosen based on their availability in the national database and their likeliness to influence the probability of treatment or the outcomes. We divided covariates into three groups as documented in Table  OA. 1 in the Online Appendix. The first group of variables includes a rich set of sociodemographic variables (such as gender, age, place of residence), and the worker's record of past vocational injuries, the worker's occupation, and their work environment. For the second group, a number of variables were extracted directly from the initial medical certificate issued after the accident in order to provide a direct measure of the injury's initial severity: the type of injury (categories: imprecise, superficial injuries, open wounds, closed fractures, open fractures, dislocations, sprains and strains, and traumatic amputations), the location of the lesion (categories: imprecise, neck, back and rib cage, upper limbs, lower limbs), the number and type of injury codes present in the initial medical certificate, and the initial number of sick leave days prescribed.
At the end of this first step, a preprocessed dataset was obtained with weights derived from CEM: unmatched observations were weighted 0, matched treated observations were weighted 1, and matched controls were weighted according to the number of matched treated and control observations in the strata and overall. We used the R package CEM version 1. 1. 9 to compute our estimations. • Step 2. Construction of a propensity score with all control variables A logistic regression predicting treatment status using all control variables was performed on the preprocessed sample. This propensity score was derived on the weighted data after the first CEM preprocessing, so that it could be focused on the determinants not accounted for by the first CEM. Propensity score values between the treated and the control groups were compared, and observations potentially dropped when out of the common support.
• Step 3. Processing of the data through CEM with important covariates and propensity score We coarsened the propensity score computed on the preprocessed sample. Several cut-offs were tested to obtain a good balance on all covariates while minimizing loss of treated sample. We applied CEM on the pre-processed sample (with weights derived from the first CEM), while including the important coarsened covariates and the coarsened propensity score. We then assessed the balance of the matched samples. • Step 4. Estimation of the Average Treatment effect on the Treated Results were estimated on the processed data (with weights derived from the second CEM). We computed a parametric ATT by conducting linear regressions with all control variables. This method is recommended to capture any remaining residual effect of these control variables on outcomes  [19, 27, 33, 34].
For instance, in Scholz et al. [4] , vocational accidents only represented 37% of the total sample, the rest consisting of non-vocational injuries of employed workers, and a few injuries of unemployed people. Yet work-related injuries are associated with specific challenges in the rehabilitation process, linked to often poorer recovery outcomes  [53] , and a complex set of shared compensations between the employer, the State and the two different insurance risks (vocational and non-vocational)  [6]. In our study, due to the rich dataset, we were able to focus on vocational injuries while controlling for potential compensation effects from vocational to non-vocational risks, in terms of healthcare use. The empirical strategy implemented in this study shows the potential for quantitative analyses based on observational data, in the absence of a RCT.
We take advantage of the large size of the available administrative dataset to find appropriate controls for the treated group. Our empirical strategy clearly remains a "second-best" option compared to a RCT, because it relies on the hypothesis of conditional independence which cannot be tested. But we are able to produce results through a combined CEM and PSM procedure, backed with careful balance checks. Implementation issues, however, played an important role in determining the final size of the treatment group, the follow-up duration, and the final decision to redesign the program. For instance, the degree of latitude and the complexity of the eligibility criteria were associated with increased workloads for case managers and subsequent delays in entry, leading to a smaller than expected treated sample.
Acknowledgments The authors thank the members of the Professional Risks Department and the  Statistical Studies Department  from the  French National Health Insurance Fund for Employees (CNAMTS)  for giving us access to the data and for their contributions to the study design. We are thankful to  Pr. Andrew Jones  for having suggested the CEM approach, to  Stephen O'Neill  for his helpful comments on a previous version of the paper at  EuHEA 's PhD Student Supervisor and  Early Career Researcher  conference (Catania, Sept. 2018), and to  Sameh Hashem  for his valuable research assistance. The paper has also greatly benefitted from comments from two anonymous referees and the journal editor.
The hypothesis formulated here is that by modeling the workers' dynamics, it is possible to extract information about the contribution of body joint movements to various ergonomic risks. Moreover, with the learned models, it is possible to predict the motion trajectory of body joints and thus detect any possible future exposure to postural ergonomic risk. For the purposes of this research, human motion modeling and trajectory predictions were made using a Gesture Operational Model (GOM)  [14] , which consists of a system of equations based on different assumptions about the dynamic relationship of body parts. The methodology was validated by evaluating the forecasting performance of the system and by improving the recognition performance of professional movements, using four datasets. The first and second datasets were taken from professional movements executed in factories concerned with television production and airplane manufacturing, respectively.
The third dataset was composed of gestures performed in a glassblowing workshop, while the fourth dataset of motion primitives, with different ergonomic risk levels, according to EAWS  [5] , was recorded in a laboratory. In Section 2, which follows, the present state-of-the-art related to motion analysis for modeling, prediction, and pattern recognition, will be presented, while the methodology and evaluation procedures we used are described in Section 3. Section 4 presents the results of the experiments conducted on the four datasets, Section 5 discusses our findings and results, followed by the presentation of our conclusions in Section 6.
Methodology Due to the nature of the hypothesis defined for this study, a physics-based approach was selected to model the dynamics of professional movements. Physics-based approaches have proved to be capable of handling joint predictions efficiently and because of the use of a transition function, they perform well with observations obtained from different environments and subjects, without extensive training datasets. This generalization capability is essential if workers from various industrial sectors are to be monitored. Moreover, by using a physics-based model, information could be extracted regarding the human dynamics and their response to risk factors, by examining the resulting trained models. In this study, human motion was represented as a sequence of human poses, where each pose was described through 3D-joint angles.
Those 52 rotations were combined to create a kinematic skeleton that included the body segments measured. The Euler local joint angles on three axes X, Y, and Z were computed through the inverse kinematics solver provided by Nansense Studio (suit software). The joint angles per time frame were then exported to Biovision Hierarchy (BVH) files. Before the analysis, an offline pre-processing procedure of the data was followed. The motion data was low pass filtered to mitigate noise, and the common zero velocity update algorithm was applied to remove the drifting caused by electromagnetic interference.
On the contrary, for the gesture vocabulary G 3 and gestures G 1,3 , G 2,1 , and G 2,3 , the subjects needed to be more precise since they placed the objects in a specific position. It has to be considered that human factors such as level of experience, fatigue, or mental stress affected how the subjects' bodies performed the gestures. Although this did not apply for the G 3 , high dexterity and technicity were required to execute the gestures effectively. The gestures from G 3 were recorded from a glassblowing expert, who performed the gestures with high repeatability and low spatial and temporal variations between iterations, to produce a carafe four times with the same specifications. Regarding G 4 , there was a low intra-class variation in this dataset since the performance of each movement was controlled.
Feedback was provided to subjects in order to perform the gesture demanded correctly. On the other hand, the inter-class variation was intended to be low, where there were only a few variations in the postures assumed in each gesture. The end in using this last dataset was to test whether the proposed methodology was able to identify the small variations between motions and provide an accurate estimate of the joints that most contributed to the execution of the 28 motions. From an ergonomic point of view, these four datasets could assist in the evaluation of human motions in industrial settings. The modeling of these motions could help in evaluating the subjects' manual dexterity in relation to the gesture's ergonomic risk. For example, as mentioned, the glassblower had a high level of dexterity for glassblowing gestures, but some observational methods could recognize that the gestures executed were ergonomically risky (e.
The motion data of sensors that provided at least one of the top three joint angles contributing the most in the response for the spine, arms, and legs parts motion was used for gesture recognition. Since one sensor provided three angles of one joint, all the joint angles of the sensor were used for training. The first combination to test for gesture recognition consisted of a minimal sensor configuration: the best sensor to measure the spine, another which was the best to measure the arms, and a third for the legs. If the recognition performance was low, an extra sensor was added to the configuration to improve the performance, or it was replaced by another of the top three sensors selected to measure its corresponding body location (spine, arms, or legs). The configuration that achieved the best performance was compared with the recognition performance obtained using all the joint angles of the sensors.
The recognition performance, by using only a minimal set of two sensors, was also computed for comparison. This minimal set consisted of two hand-picked sensors, which provided the Euler joint angles of the right forearm (RFA) and hips (H). The sensor placed on the right forearm was chosen since most of the subjects in all datasets were right-handed, and the hips sensor was chosen because the origin of all movement of the spine starts from the hips. To determine the best HMM setting for each gesture vocabulary, both ergodic and left-right topologies were tested, in addition to a different number of hidden states. The performance metric used consisted of the F-score. In the training phase of HMM, each professional gesture G v,c , where v ∈ [1, 4] indicates the gesture vocabulary and c ∈ N the gesture of the G v , is associated to an HMM.