dataesr/scientific-paragraphs-categorization huggingface dataset:
The model was validated on the YH participants and then applied on the OH and PwP participants. We were not able to do 3D motion capture with the Codamotion system in OH and PwP groups, primarily because the data was collected from people's home. This was seen as appropriate since the participant groups had difficulty travelling. We aimed to collect the data with minimal disruption and given the problems of setting up the Codamotion system in the home environment, we intentionally omitted this stream of data. However, the validation of wearable sensors using motion capture data collected from YH group gave a strong indication that our kinematics estimation method using the proposed model works as seen from the small NRMSE (Table  1 ) and Bland-Altman plots (Fig  7 ) and hence further validation for the OH and PwP groups was not required.
Study design The SEYLE study is aimed at the prevention and early intervention of mental problems, suicide, and risk behaviours [registered at the US National Institute of Health (NIH) clinical trial registry (NCT00906620), and the German Clinical Trials Register (DRKS00000214)]. SEYLE is a randomized controlled trial (RCT) including three different school-based interventions and one control group. For the present study, only participants who were randomized to either the ProfScreen intervention or the control group were included to examine the effectiveness and feasibility of this particular two-staged intervention, which had specifically been developed to facilitate referral to appropriate professional mental healthcare. Wasserman and colleagues described details of methodology and interventions of the SEYLE study, including the other two intervention groups, the gatekeeper training "Question, Persuade, and Refer" (QRF) and the awareness training "Youth Aware of Mental Health Programme" (YAM)  [28].
Description of samples and baseline differences between the ProfScreen and control group Of the total N = 12,395 SEYLE study participants, 3070 were randomised to the ProfScreen and 3257 to the control group. Of those, 4172 (65. 9%) completed the 12 month follow-up, were not emergency cases, and had complete data. Among those complete cases, 2583 (61. 9%) students were considered at-risk for mental problems or risk behaviour at baseline; comprising 1314 (50. 9%) students of the Prof-Screen and 1269 (49. 1%) of the control group. 535 (40. 7%) students of the ProfScreen group attended the clinical interview and 149 (27. 9%) of these were referred to subsequent treatment (Fig. 1 ). Subsequent data analyses refer to the 2583 students that were at-risk for mental health problems or risk behaviour at baseline.
Acknowledgements The SEYLE project was supported by the European Union through the Seventh Framework Program (FP7), Grant agreement number HEALTH-F2-2009-223091. The study sponsor had no role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication. Site leaders are Danuta Wasserman (NASP, Coordinating Centre), Christian Haring (Austria), Airi Varnik (Estonia), Jean-Pierre Kahn (France), Romuald Brunner (Germany), Judit Balazs (Hungary), Paul Corcoran (Ireland), Alan Apter (Israel), Marco Sarchiapone (Italy), Doina Cosman (Romania), Vita Postuvan (Slovenia) and Julio Bobes (Spain). All authors confirm that there is no potential, perceived, or real conflict of interest. Special acknowledgments regarding the study go to all staff and participants that were involved in data collection.
Patients Patients were included in the present study if they were older than 18 years of age and if a fungal organism was isolated on catheter blood culture between January 2014 and December 2016 in the Bordeaux University Hospital. Catheter-associated fungemia could be either CRBSIs, either secondary fungemia if the source of infection was visceral localization. A visceral localization was described by the presence of a localization on an imaging examination and/or a microbiological documentation of an organ/ uid. The following data were retrospectively collected from the patient's medical le: underlying comorbid conditions (cardiac, renal, respiratory insu ciencies, human immunode ciency virus infection, ongoing solid cancer or hematological cancer, diabetes mellitus, chemotherapy), predisposing host conditions (neutropenia within 1 month before, antibiotics within 3 months before central catheter fungal infection, intravenous drug abuse), biological parameters (albumin, creatinine clearance), characteristics of infected central catheter, results of investigations and mortality at 3 months.
Statistical analysis All the data were recorded and entered into Microsoft Excel format and analyzed using STATA® (Stata Statistical Software, version 13. 0, College Station, Texas, USA). Continuous variables were summarized by means (standard deviations) and medians (interquartile range IQR), and categorical variables as counts (%). A multivariate analysis was performed to identify risk factors for overall mortality at 3 months using χ 2test for categorical variables and Student's t-test for continuous variables. Variables potentially associated with mortality in univariable comparisons (p < 0. 05) were included in the multivariable model. A P-value of < 0. 05 was considered statistically signi cant. Given the retrospective and observational methodology of the study, no speci c patient consent was collected, and no ethics board approval was required in accordance with French laws.
Acknowledging these issues, in a key report on the future of public health post-COVID, Canada's Chief Public Health O cer highlighted the importance of coordinated responses by working collaboratively with all levels of government and key stakeholders  [49]. By analyzing the development and implementation of promising intersectoral initiatives to meet the diverse needs of refugees, asylum seekers, and migrants without status, the present project is directly in line with these public health recommendations. Impacts of the pandemic on intersectoral services for refugees, asylum seekers and migrants without status Enhancing intersectoral action is a major lever in the response to pandemics  [16]. Community mobilization, as a pillar of intersectoral action, is an essential instrument for pandemic responsiveness  [50] [51] [52].
Data analysis Research activity #1 Observational notes and outcomes of deliberative workshops will be added to data sets for research activities #2 and #3 and analyzed in the same way. We expect a selection of approximately 20 initiatives in total (across the three cities and re ecting the two aforementioned categories of intersectoral initiatives). Research activities #2 and #3 Data will be transcribed, then coded using QDAMiner, and analyzed using thematic analysis  [86]. The components of the adapted framework by Mirzoev & Kane (2017) will inform our main thematic categories, paying particular attention to the identi cation of co-evolution processes within intersectoral collaboration mechanisms, and in the context of the pandemic. A deductive-inductive approach will be employed and informed by the aforementioned framework while also leaving the possibility for empirical data to bring out additional themes and sub-themes.
Research activity #4 The data collected from discussion forums will be synthesized and reported using factsheets that will be made available to CBOs, public organizations, and policy-making bodies. Based on issues identi ed in these forums, we will also adapt a monitoring tool based on the Pluye & Ridde (2015) model  [87]. This model proposes to evaluate the elements that are favorable or unfavorable to sustainability and/or implementation (e. g. , stabilization of organizational resources, incentives or bene ts for the actors) on a regular basis, and will be adapted to the needs of partners.
Our research partners will help mitigate this issue. For instance, our collaboration with CBOs and representatives of refugees and asylum seekers in all three cities will help locate the neighborhoods where prospective participants live, and identify the most adequate ways to approach them and include them as research participants  [84]. Second, we will closely monitor the evolution of COVID-19 and its potential impact on the research. The research team will meet each month to assess the situation, adjusting our plans in strict compliance with public health measures. We will thus collect data and hold research meetings using either adequate equipment to maintain physical distancing, or videoconferencing and other remote communication tools. However, using these tools may incur di culties for people with digital literacy issues, particularly in the case of focus groups.
Acknowledgements We would like to thank the colleagues who contributed to prior versions of the present protocol:  Lourdes Rodriguez del Barrio ,  Jill Hanley ,  Janet Cleveland ,  Esther Mc Sween-Cadieux , and  Christine Blaser. We also acknowledge trainees and students participating in the project:  Achille Dadly Borvil ,  Lama Boustani ,  Ibrahima Diallo , and  Anne Drujon.
Static balance measurement The statics balance was measured using a posturographic platform  (Fusyo, Medicapteur, Balma, France, 40 Hz)  in the eyes open condition. The participants stood barefoot with two legs on the platform and were asked to look steadily at the fixed points on the wall in front of the participant. The center of pressure (COP) displacement was measured during 25. 6 s  (Bernard et al. , 2010)  and processed with the software W-IN POSTURO (Medicapteur, Balma, France). From COP displacement, several indicators were calculated, including the surface of displacement (SURF), the total length of displacement (LXY), length of displacement on the sagittal axis (LY), mean position on the transversal axis (X mean ), mean position on sagittal axis (Y mean ), length of displacement as a function of surface (LFS), the COP speed of displacement on the sagittal axis (VFY).
Foot pressure parameter measurement Foot pressure was measured by W-INSHOE plantar sensors (Medicapteur, Balma, France, 100 Hz) during the walking phase of the Time Up and Go Test (TUGT) under standard conditions  (Podsiadlo and Richardson, 1991). Nine pressure sensors were placed on the 3-foot locations, including hallux, forefoot, and rearfoot (Figure  1A ). Data delivered by sensors concerned with pressure and duration. First, sensors were grouped according to their localization to compose the forefoot and the rearfoot to obtain a biodynamic pattern. Then, all parameters were normalized (%) according to total plantar pressure and total foot contact duration during the stance phase of the walking task. Six parameters were extracted: hallux pressure, forefoot pressure, rearfoot pressure, total pressure, rearfoot, and forefoot relative contact time.
The use of inertial measurement units (IMUs) to compute gait outputs such as the 3D lower limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences of amplitude.
The aim of this study was to assess the validity of a new 3D lower limb kinematics computation method on a healthy population against the silver clinical standard: the optoelectronic system with markers on anatomical landmarks. The computation method was also tested on optoelectronic data from clusters of markers, in order to separate the SFA and S2S sources of errors. The proposed method was thought to be suitable for pathological subjects and clinical settings, keeping the number and the difficulty of the instructed tasks as low as possible. This study insisted on providing the same kinematic outputs as commonly provided by conventional gait analysis  [8]. Thus, pelvis and foot kinematics was assessed in addition to hip, knee and ankle kinematics.
The tridimensional acceleration and angular velocity were acquired at 256Hz, with ranges of ±16g and ±2000°/s respectively. Reflective markers were placed according to the Conventional Gait Model (CGM) 1. 0  [12]  with the addition of two markers on the first metatarsal heads to help dealing with any confusions between the 'TOE' marker and the cluster markers (Figure  1 ). Marker trajectories were measured at 100 Hz. As shown on Figure  1 , participants also wore pressure insoles (Moticon, Munich, Germany) fixed to light sandals, and an IMU on the thorax fixed with a GoPro harness (GoPro, USA). This was for the need of another project. Insoles and upper-body inertial data were not analyzed in this study. The whole dataset including marker trajectories, IMU data, and insoles data is available online (Grouvel G.
Sensor-to-segment (S2S) calibration Several steps for S2S calibration were proposed. Each of them consisted of determining a rotation between the actual sensor frame and the desired segment's frame. The orientations computed from the clusters underwent the same rotations in order to check the relevance of the calibration performed on each sensor. 1-Alignment with gravity: During the first calibration standing posture, the segment's vertical axis (Z) is supposed to be aligned with gravity measured by the accelerometer. A rotation was applied to the sensor data to align the vertical axis of the sensor with the vertical axis of the segment. This rotation was also applied to the cluster-based quaternions. 2-Alignment with the segment's mediolateral (Y) axis: During the walking trials, the feet, shanks and thighs' mediolateral axis was determined by the principal axis of the measured angular velocity, supposing that the movement occurs mainly in the sagittal plane for these segments.
Data analysis Eleven kinematic variables per side were compared between the 3 approaches (reference kinematics from the anatomical markers, kinematics from the clusters, and kinematics from the IMUs): pelvis ante/retroversion, pelvis obliquity, pelvis in/external rotations, hip flex/extension, hip ab/adduction, hip in/external rotations, knee flex/extension, knee ab/adduction, knee in/external rotations, ankle dorsi/plantar flexion, and foot progression angle. These outcomes were selected as they are of interest for clinical gait analysis  [17]. The foot progression angle was defined as the angle between the pelvis anteroposterior axis and the foot longitudinal axis. The kinematic data was represented according to the gait cycle (%) and averaged for each participant. The RMSE, the RMSE centered at the mean (as proposed in  [18] ), the Pearson's correlation coefficient (CC) and the absolute difference in ranges of motion (ΔROM) were computed to evaluate respectively the global accuracy, the accuracy without any offset, the shape of the curves and the amplitudes of the curves.
In any case, magnetometer-free systems are described as relevant for capturing data for short term  [34]. The main issue with our method concerned the offset, i. e. the constant absolute angle between the IMU kinematics and the reference kinematics. This was especially visible on the pelvis ante/retroversion, with a mean absolute RMSE of 14. 1 ± 2. 8° and a mean centered RMSE (removing the average mean of each curve) of 1. 2 ± 0. 3°. Berner et al. related very similar errors and tried to compensate them taking advantage of optical data during calibration trials  [35]. This adjustment permitted to considerably reduce the overall errors (<5°, except hip rotation). The problem with this proposition is the need for an external device which compromises the use of the gait analysis system outside of standard laboratories.
We can thus infer that this came from SFA inaccuracies. Similar errors were found in other studies at the ankle flexion/extension level, specifically at mid and terminal swing phases  [42, 43]. This could be due to the abrupt difference of dynamics of the foot between the stance and swing phases, as it is known that SFA highly depends on dynamics of motion  [2]. The sampling rate was found to have a great effect on Madgwick's algorithm performance in dynamic conditions  [15]. To adapt the sampling rate could constitute an axis of improvement of the method. This may also be caused by inaccuracies coming from the sensors' common frame setting and the sensor-to-global frame calibration. Indeed, these two calibration steps were only performed on IMU data, and not on cluster data since markers are already measured in the global frame.
This may provide errors when assessing pathological populations. Finally, we did not assess the reliability of our method due to the heaviness of such a protocol. A new 7-IMU system composed of accelerometers, gyroscopes and associated SFA and S2S algorithms has been proposed in this work. Far from being the first of its kind, our method found its novelty in providing a complete 3D lower limb kinematics, similar to conventional gait analysis outcomes, with no need of an external device or high expertise for data acquisition. The absolute errors of our system against the clinical reference were globally too high to be used for clinical interpretations. McGinley et al. postulated that absolute errors of a maximum of 5° were seen as clinically reasonable  [44]. We did not reach this objective.
Background: Effective collaboration between general practitioners (GP) and nephrologists is crucial in CKD care. We aimed to analyse GPs' and nephrologists' presence and involvement in CKD care and assess how they intertwine to shape patients' trajectories. Methods: We conducted a mixed-methods study that included all patients with CKD who started dialysis in France in 2015 (the REIN registry) and a sample of nephrologists and GPs. We quantified professionals' presence through patients' reimbursed healthcare from the French National Health Data System, 2 years before and 1 year after dialysis start. Involvement in CKD care was derived from the nephrologists' and GPs' interviews. Results: Among 8856 patients included, nephrologists' presence progressively increased from 29% to 67% of patients with a contact during the 2 years before dialysis start.
A written information document to present the study and to ask whether the physician agreed to be interviewed was sent to the identified healthcare professionals and if they agreed, an interview was scheduled. At the start of the interview, verbal consent was sought for the audio recording of the conversation. Between April 2020 and April 2021, semi-structured interviews by telephone were carried out by one of the authors (M. R. , PhD with 3 years of qualitative research experience and with no prior relationship with participants). Interviews and data collection continued until data saturation was reached (i. e. when additional data did not lead to any new theme or development). 19 During that data collection process, only one GP refused the interview when solicited.
Analyses After the initial reading of the full transcripts, iterative rounds of reading, word by word, were performed and data were categorised into themes to describe all aspects of the content. 20 The analysis was carried out by M. R. using NVivo v. 12. 4. Coded themes were refined after feedback and discussion with A. C. , F-X. S. , and L. L. These themes were then used to describe the forms of involvement in CKD care by both professionals. These qualitative findings were presented at several national healthcare congresses to externally validate them through discussions with professionals (outside of Brittany). Quantitative component: to describe the GPs and nephrologists' presence in the care of patients with CKD using healthcare data utilisation A quantitative analysis was carried out using data on healthcare utilisation by all patients with CKD who started dialysis in France in 2015 to validate the previously determined forms of involvement of GPs and nephrologists.
The aim of this quantitative analysis at the nationwide level was to determine how many patients saw a GP and a nephrologist during the period of transition to dialysis and to identify who prescribed what and in which relative proportion. First, all ⩾ 18-year-old 2015 incident dialysis patients were identified in the REIN registry. The REIN registry records all patients who start maintenance kidney replacement therapy in France and collects baseline data on patients, treatments, and outcomes (e. g. death, transplantation) (ethical approvals CCTIRS 03-149 and CNIL N° 903188). 21 en, healthcare utilisation data were obtained from the French National Health Data System (SNDS) database. This nationwide medicoadministrative database contains data on all care utilisation in inpatient (e. g. hospitals, hospices, clinics) and outpatient (i.
e. community-based, office-based) services. 22 To this aim, a deterministic indirect record linkage was performed to retrospectively link the SNDS healthcare data and the REIN data of all incident dialysis patients in 2015. 23 Unmatched patients and patients without information on dialysis start were excluded. The study population was previously described elsewhere  11  (flowchart available in supplemental material 2). All reimbursements of consultations, laboratory tests, and drug prescriptions were extracted as well as the prescribers' medical specialty. The study period included the two years before and the first year after dialysis start.
Analyses The percentage of patients who had a consultation with a GP and/or a nephrologist, and the percentages of laboratory tests and drugs prescribed to these patients by a GP or a nephrologist were reported for each quarter of the study period. We also examined separately drugs acting on the renin-angiotensin system (angiotensin-converting enzyme inhibitors, ACEi, and angiotensin receptor blockers, ARB, ATC code C09). All analyses were carried out with R v3. 6. The Results section describes the two qualitative and quantitative components together (i. e. integration process). In addition, a sensitivity analysis was carried out to ensure that the quantitative data (patients from the entire France) could be compared with the same data for the subset of patients from Brittany (the region where the interviews were carried out for the qualitative component) (available in supplemental material 3).
Nephrology is a bit off-putting because it is not clinical examination, it consists of interpreting laboratory tests. (N16) Reaching nephrologists, through a call or a referral, was a way for GPs to reduce the uneasiness and by doing so, they relinquished part of their usual strong and continuous form of involvement. This was facilitated by the fact that these patients represented only a small part of their patient base. Nephrologists' control of the therapeutic strategy and GPs' valued place in the care. In an emergent form of involvement in the care, GPs interviewed valued their place in CKD care through the work of screening, guiding the patients through their care, coordinating (i. e. repeating and reviewing messages coming not only from the nephrologist but also from other specialists), and through the unique trust they developed with their patients: We centralise all the information, we are like a database: all the specialists' notes end up with us and sometimes there are some messages that are not necessarily well understood, because it goes fast, it's complicated, and there is a lot of information coming in one go.