dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work was supported by the  Mobilise-D  project that has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement No. 820820. This JU receives support from the  European Union 's  Horizon 2020 research and CB has done consultation work for Philipps Healthcare, Bosch Healthcare ,  Eli Lilly  and Gait-up; he has received speaker honoraria from  Amgen ,  Pfizer  and  Nutricia. LS is working as the  Chief Technical Officer for Ro ¨lke Pharma. JMH reports having submitted a patent for assessment of mobility using wearable sensors in 400 Parkinson's disease; the intellectual property rights 401 are held by the  Tel Aviv Medical Center. GC has received consulting and speaking fees from  Novartis ,  Sanofi Genzyme ,  Genzyme Corporation ,  Merck KGgA ,  Merck Serono SpA ,  Celgene Group, F.
Data collection Data will be collected from the interviews with each health professional. Each interview will be conducted by the main investigators (GL or MC) or a psychologist recruited for the study according to the same plan using a specifically designed guidebook. The guidebook was developed by the main investigators of the study (GL, MC) according to the literature review. Particular attention was given to the wording of the questions to prevent bias, such as desirability bias. After the first few interviews, the guide may be adapted based on early feedback. The guidebook is presented in box 1. The following items are explored: (1) respondents' characteristics (age, gender, profession, years of practice and type of practice); (2) disease perception and the perception of parental responsibility for the child's oral health; and (3) barriers and facilitators to family behaviour changes and health professionals' emotional consequences.
Several test interviews will be conducted prior to the study to validate the guidebook and calibrate the investigators. Face-to-face interviews will be conducted by the main investigator (GL), and video interviews may be conducted depending on the COVID-19 pandemic situation. All interviews will audio recorded. The interviewer will be in an active listening position (using rewording and reminder techniques) and will record non-verbal communication items (position, attitudes, facial expression, intonation, etc). Interviews will be conducted according to the thematic saturation principle. Saturation is defined as information redundancy  29  and occurs when the data collected during the interview no longer provide new codes or themes related to the topic under investigation. All interviews will be completely transcribed verbatim and stripped of identifying information.
Data analyses Verbatim analyses will be performed using NVivo qualitative analysis software. A reflexive thematic analysis of the data will be conducted using the six-step method of Braun and Clarke. 30 Data coding will be achieved in a data-driven manner. As the analytical process is not based on a theoretical framework, this open-coding technique aims to stay as close as possible to the meaning of the data. The investigators (GL, MC and psychologist) will code the first interviews independently before working more collaboratively. Similar codes will then be associated with coherent themes, defined as meaning-based patterns. 30 heme development in reflexive thematic analysis involves researchers' subjectivity (knowledge, theoretical assumptions, etc) because their goal is to give an interpretation of the dataset rather than to summarise it.
Ethical issues and dissemination Participants will receive and sign informed consent forms regarding the objectives of the study and the right to deny further participation or withdraw at any time. All participants' signed consent documents will be collected before the interviews. Ethical approval was obtained from the research ethics committee of the Department of Family Medicine at University Lyon 1 (no. 2021-09-09-03). The protocol is registered on ClinicalTrials. gov (no. NCT05284279). The first interviews should be conducted in April 2023. Data collection and analyses will last 12-18 months. The results will be published in peer-reviewed journals and presented at scientific meetings.
Strengths of the study Following Stangl et al's theoretical stigma and discrimination model, the results will be presented in a standardised manner using a common model for all researchers in the field. This approach will help to pool and compare data with other researchers working on the topic of oral health-related stigma or other health discrimination. When more data are collected on oral health-related stigma, the framework will allow the synthesis of varied studies and provide a clear and structured overview for actors involved in the issue (researchers, healthcare workers, decision-makers, intervention implementers). In this way, the results may highlight a lack of data on particular aspects of the topic, which would consequently require further research. Conversely, the data may help to identify areas where sufficient information has been gathered to support the development and implementation of interventions intended to increase the quality of disease management.
Limitations of the study In this study protocol, we chose to define saturation in a rather conventional manner based on the concept of information redundancy. 29 In this context, saturation is thought to occur when the dataset no longer provides additional information, codes or themes. Numerous authors refer to data saturation as the gold standard for determining sample size, while qualitative checklists (such as the COREQ checklist) present it as a key criterion for methodological quality. However, this approach to saturation has increasingly been questioned by authors such as Braun and Clarke, who consider it irrelevant in the case of reflexive thematic analysis. 38 In this type of analysis, themes result from the interpretative choices of the researcher since theme development Open access relies on data as well as the researcher's experience, knowledge and Thus, new interpretations can continually be discovered, which directly contradicts the very principles of saturation.
Centers should have access to pediatric intensive care units, a minimum of 1 million population base for referrals of specialized pediatric urology and/or pediatric surgery cases, and in an area where COVID-19 closedown required cancellations of all elective surgery (only running emergency and imperative cases). Chief of department in all centers approved retrieval of information regarding waiting lists to surgery and data collection, and surveys were compiled and answered by the co-author representing each center. All reported data from waiting lists was anonymized on a patient level. The study design was a longitudinal, prospective study based on pediatric urology cases waiting lists in different European centers. Centers participated over a 13-months study period (March 2020 to April 2021).
To compare with pre-COVID-19 capacities, as well as normal seasonal variation, this data was presented per annum. Moreover, to compare with the corresponding outpatient activities, data on external referrals for pediatric surgical evaluation during the same period was obtained from two participating centers. Synchronously, qualitative online surveys regarding financial, logistic, and organizational changes were obtained using Redcap R (questions asked can be found in Figure  6 ). The participants were surveyed at four timepoints during the study period (June, October, January, and April, respectively).
Statistics Descriptive statistics were based on absolute numbers from reported waiting list at the five study timepoints. Given the explorative nature of our data, further advanced hypothesis testing models were not considered appropriate. Centers that were not able to provide complete waiting list data on the respective procedures for all timepoints were not included in the quantitative results. Since data from large-volume centers might dominate the statistical tendencies in the general results, we assessed the proportional waiting list changes, to level out possible skewing from varying center sizes and operative volumes. The mean of the percentual waiting list changes from each center were assessed with reference to the study baseline (March 2020). Due to low sample-sizes and high variance in rare procedures, this was only deemed suitable for a limited number of high-volume procedures, defined in this study as >10 patients per center waiting on average at each timepoint (i.
Data sources We collated data on number of sexual partners reported in a continuous scale in MSM surveys on sexual health and behaviour. We contacted epidemiologists and social researchers from the EMIS network  [1] [2] [3]  across Europe and Canada (typically at least one academic and/or governmental partner per country) to identify behavioural surveys among MSM conducted in their respective countries, and asked them which of the national surveys had used open write-in fields for the numbers of sexual partners. Thirteen national and multi-national surveys were identified as eligible, and data of the partner number write-in fields were obtained from these surveys and included in our analyses  [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. The surveys were undertaken between 1995 and 2019 across Europe and in Canada.
Next, we calculated the mean number of sexual partners by time frame (over the previous 12 months vs. the previous 6 months), by survey (for each of the thirteen included surveys), and by type of sexual partner (any sexual partner vs. CAI partners) for one of the commonly reported set of categories to assess potential differences in the means by each of these aspects and to contrast the robustness of the first findings. Respondents reporting 'zero' partners were excluded from the overall N and, thus, from the overall mean and all sample share proportions. Respondents with missing data on partner numbers were also excluded.
Manipulation of ingressiveness Ingressive syllables were manually annotated and manipulated in one of two ways. In the original condition, they were attenuated by 0, 9, or 18 dB by means of separating, processing, and then cross-fading egressive and ingressive parts of each laugh with an R script. In the noise condition, there was an additional processing step: the ingressive syllable was mixed with a synthetic noise with the same RMS amplitude and the same spectral envelope, which was achieved by using the transplantFormants() function in the R package soundgen  (Anikin 2019). The relative contribution of noise was 0% at attenuation = 0 dB (i. e. unmodified original recording), 50% at attenuation = 9 dB, and 100% at attenuation = 18 dB (i. e. only noise). In other words, in addition to making the ingressive syllable quieter, in the noise condition it was morphed into an aspiration-like sound with the same formant structure.
Data analysis Each of the three experiments (laughs, cries, and moans) was analysed independently. Unaggregated responses were analysed with Bayesian multilevel zero-oneinflated beta models using the R package brms  (Bürkner 2017). The model predicted the rating as a function of Condition (2 levels: original or noise), Attenuation of ingressive syllable (linear effect: 0, 9, or 18 dB), and Scale (5 levels), with all possible interactions. Because an attenuation of 0 dB means that the sounds were identical in both conditions, the corresponding beta coefficients were set to zero as part of prior specification, forcing the separate regression lines per condition to converge at zero (Figure  2 ). The effects of condition and scale were assumed to vary across subjects and across prototypes, and the effect of scale could also vary across individual stimuli.
With cerebral infarct, endovascular treatments had an independent positive effect, both on the recovery of balance (aHR 1. 65, 95% CI 1. 13-2. 4, P = 0. 009) and gait (1. 78, 1. 24-2. 55, P = 0. 002). Conclusions: WMHs magnify balance and gait disorders after stroke and worsen their recovery. They should be better accounted for in post-stroke rehabilitation, especially to help establish a prognosis of mobility. ClinicalTrials.
Study design This was a monocentric observational study involving data from the cohort Determinants of Balance Recovery After Stroke [DOBRAS]; ClinicalTrials. gov: NCT03203109) with participants comprehensively assessed in routine care in the rehabilitation ward  [2]. For this study, we used balance and gait data collected at 30 (AE3) days after stroke (D30), then at discharge (within the last 3 days) in the neurorehabilitation ward. Assessments were performed by trained and multidisciplinary examiners, with blinding to other data and the study hypothesis. We also used demographic data and main clinical and imaging features. The sample size of the DOBRAS cohort was planned to conduct multivariate analyses focused on balance and gait recovery in a large series of individuals (! 200 observations), including multivariate (! 3 variables) Cox models performed in this study.
Ethical considerations and reporting The DOBRAS cohort was approved by our institutional review board (CHU Grenoble Alpes) who validated the ClinicalTrials. gov registration (NCT03203109). The study was also registered at the National Committee for Informatics and Freedom (Commission nationale informatique et liberte´; CNIL-No. 2014874-v1) and was performed in accordance with the Helsinki Declaration. According to French law, observational studies do not require approval by a national ethics committee, provided that participants have been informed of the specific research and are not opposed to use of their data. All eligible individuals were informed of the DOBRAS study (orally and in writing) and those who did not want to participate signed an opposition form.
Participants From January 2012 to October 2019, we included 210 consecutive individuals (age 18 to 80 years), with a first-ever unilateral hemisphere stroke analyzed by MRI. Exclusion criteria were recurrent stroke, complications at the acute stage (malignant infarct, cerebral herniation, severe subarachnoid hemorrhage and hydrocephalus), dementia, previous disability interfering with balance or vestibular disorders, and unstable medical condition or psychiatric problems jeopardizing data reliability (Fig. 1 ). These conditions were obtained from the hospital electronic file describing the history of every patient, by interviewing patients and relatives, and by a systematic clinical examination. Participants followed a personalized rehabilitation program, taking into account deficits and activity limitations, capacities to face intensive rehabilitation (fatigue or transitory medical problems), individual recovery, and the most appropriate guidelines.
Any disagreements were resolved by consensus. Drawings were performed on axial slices of a T1-weighted MRI template from the Montreal Neurological Institute (https://www. mcgill. ca/bic/neuroinformatics/brain-atlases-human) normalized to Talairach's space. CST disruption was analyzed with the ''Tractotron'' tool of the BCBToolkit software (http://toolkit. bcblab. com/), which determines the disruption induced at the level of a given lesion. Each participant's lesion was compared with an atlas of white matter tracts for each voxel. As suggested, we analyzed both the proportion and probability of the ipsilesional CST disrupted by the stroke  [38]. The proportion corresponds to the number of damaged voxels in the CST divided by the total volume of this tract. The probability corresponds to the lesioned voxel with the highest percentage value.
Differences between participants with no/mild and moderate/severe WMHs were compared by the log-rank test. The relationships between balance and gait disorders (at D30 and discharge) were tested by linear regression. Continuous data are presented as median (Q1-Q3) and categorical data as number (percentage, %). Two-sided P < 0. 05 was considered statistically significant. Statistical analyses involved using SPSS 24. 0 and Stata 15. 1. The data that support the findings of this study are available from the corresponding author upon reasonable request.
Results The median age of the 210 participants was 67. 3 years (57. 4-72. 7); 68 (32%) were females. In total, 172/210 (82%) had a brain infarct; 65 (38%) received thrombolysis and/or thrombectomy. The initial stroke severity was moderate to severe (median NIHSS on D1 12  [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] ). No data were missing for the primary analyses. Complementary analyses with stroke volume and CST disruption were conducted on the 182/210 observations of participants who had a second MRI (87%). To ensure the validity of the complementary cox model for imaging data, we analyzed the data sensitivity within the frame of the whole series. Sensitivity was high: 0. 88 and 0. 87 for the endpoints of single limb stance and independent gait, respectively. These results indicated that missing data for brain imaging were randomly distributed, inducing no bias.
Introduction The Anesthesia Information Management System (AIMS) has gradually replaced the paper records in the last decades  [1, 2]. This electronic version of the anesthesia record captures data automatically from clinical monitors and facilitates the documentation of the anesthesia procedure such as the main stages of the surgical intervention and anesthesia management. The benefits of an AIMS include improved readability of the anesthesia record and greater efficiency in documentation efforts  [3]. Anesthesia records are mainly used for reviewing single patient record but also help searching, querying and retrieving data from thousands of cases to conduct retrospective analyses for clinical research or evaluate compliance with guidelines  [4] [5] [6]. The main characteristics of the data recorded in the operating room are their sampling frequency and accuracy, with one measurement every 30 s for signals such as heart rate or invasive blood pressure.
This very fine data is often transformed into new variables and more workable information. For example, the arterial pressure signal is computed into comprehensive hypotension events, such as the time below or beyond a threshold, the number of episodes, or the area under the curve  [7]. Thus, the reuse of data collected automatically by AIMS has allowed to conduct several studies with thousands of interventions and to identify links between haemodynamic variations (e. g. hypotension) and negative post-operative outcomes (e. g. death, acute kindey injury)  [8] [9] [10] [11]. Reusing AIMS data faces several challenges. First of all, the data produced by the software are stored in a secured proprietary system, where it is impossible to add or modify the structure and the data. Secondly, even though AIMS provides analytical functions to count procedures or patients based on simple criteria (e.
g. date, demographics or care units characteristics), the computation of hypotension involves more complex algorithms and larger volumes of data which could be time-consuming and slow down the performance of the software in its current use. In order to overcome the problems mentioned previously, a data warehouse is created in combining multiple operational databases and acts as a single and consolidated reference data to produce statistics and reports. For this, an Extract-Transform-Load (ETL) process extracts all relevant data from operational databases, applies cleaning and transformation rules, and finally loads it into the data warehouse. Data warehouses were initially implemented by industry to assist in decisionmaking  [12] , and over the past 20 years, hospitals have also adopted this technology to facilitate the reuse of administrative and clinical data for research purposes  [13] [14] [15] [16].
Design and data model The need for the warehouse arose from an analysis of the recurring needs expressed by physicians. They highlighted the need for indicators derived from haemodynamic and ventilation signals-hypotension, hypertension, bradycardia, tachycardia, oxygen desaturation, low tidal volume. In addition to the details of the operating room period, it was necessary to integrate data on the patient's outcome, such as admission to intensive care, length of stay, mortality, organ support, biology results, and transfusions. These additional data come from other software, developed by different editors, and with each time, a database structure that is specific to the software. A concrete analytic need was to be able to compare the duration of hypotension across multiple thresholds with in-hospital mortality.
Data sources The main source of the data warehouse was the AIMS, DIANE ANESTHESIA (Bow Medical, Amiens, France)  [18]. This software is exclusively dedicated to the documentation of anesthesia procedures. Different modules collect and centralize all data referring to one case, from pre-anesthetic evaluation to discharge from post-anesthesia care unit (PACU). The data collected includes patient demographics and history, continuous monitored parameters (e. g. heart rate, blood pressure, respiratory rate, tidal volume), drugs administrations and main steps of anesthesia and surgery procedures. Intraoperative data were supplemented by hospital discharge reports from the Programme de Médicalisation des Systèmes d'Information (PMSI). These reports include all hospital stay characteristics, such as diagnoses [based on the French edition of the International Classification of Diseases 10th edition (ICD10)], medical procedures [based on the French terminology Classification Commune des Actes Médicaux (CCAM)], type of medical unit (e.
g. intensive 1 3 care), admission and discharge dates, status discharge (i. e. home, transfer or death). In addition to these two data sources, other data were also integrated into the data warehouse. For example, we added biology results (e. g. creatinine) or transfusion data from two other hospital software in order to assess the impact of hypotension on renal function. The hospital information system is not yet fully computerized and some information is still collected in paper format (e. g. patient follow-up that has no impact on billing). In addition, some software does not provide automatic extraction solution. Therefore, manual collection may still be required to add this data to the data warehouse. Because of the time-consuming aspect, this solution is only feasible for a limited number of records.
Technologies All databases (i. e. mirroring databases, staging area, data warehouse, datamarts) were stored on Oracle 11G (Oracle, Santa Clara, United States). The ETL process was conducted with OpenText Integration Center (OTIC, Open Text Corporation, Waterloo, Canada). Extraction for cohort creation were realized in SQL and statistical analyses were performed in R or SAS (SAS Institute, Cary, United States). OTIC offers 4 modules for (i) administration (i. e. creation and project management), (ii) ETL development, (iii) scheduling of automated loads of the data warehouse, and (iv) load execution reporting, with detailed logs of all programs (i. e. execution time, Oracle error, number of records loaded). We set up a development environment and a production environment. Each environment contains a database server and an OTIC server.
ETL process The ETL process consists of 5 loading flows which are described in Table  1  and the following paragraphs. AIMS and PMSI data were loaded in the data warehouse with history since 2010, through an automated ETL process, executed each week (flows 1 and 2). In a second step, the data warehouse is supplemented once a year with features of high-level of information (flow 3). Then, row-oriented records of the data warehouse supply the datamarts after a pivot operation to offer wide tables with column-oriented features (flow 4). At the end of the chain, datamarts are queried on request to produce data tables for statistical analyses or dashboards (flow 5).