dataesr/scientific-paragraphs-categorization huggingface dataset:
Strategies to improve adherence to interventions {11c} Activity monitoring and exercise surveillance after 6 months At the end of 6 months, participants in both groups will be encouraged to continue with their physical activities. Compliance will be evaluated using the Polar OH1 activity tracker (©Polar Electro) to record physical activity frequency, duration and intensity for 12 months in total. Patients in both groups will be provided with a Polar OH1, shown how to use it and asked to wear it during all physical activity sessions. After each session, they will be asked to connect to the device manufacturer website via smartphone or computer to upload information. For experimental group participants, data will be analysed weekly to determine if they are exercising as recommended. If necessary, the participant will then be motivated to continue/increase during the next telephone coaching session.
Concealment mechanism {16b} Participants will be randomly allocated into either group using Ennov Clinical, an online, central randomisation service (Ennov Clinical  [23] ). Connection will require a login, password and study number which will be provided by the data manager from the Vendée Hospital Research Department. The following patient information will be uploaded to Ennov Clinical for the randomisation: -First name and surname initials -Month and year of birth -Fulfilment of the inclusion criteria (yes).
Data management {19} All data will be patients will receive an identification number based on their initials and the order and centre of their inclusion. The number will be generated by Clinsight software, an online, electronic case report management system (eCRF; Ennov Clinical). This code will be the only patient identification information used in the eCRF; all documents used in the eCRF will also be coded in this manner. One eCRF will be created for each patient and will contain all information required for analysis of the exercise protocol data. The eCRF will indicate if the protocol has been respected, if there are any discrepancies and all data required for the statistical analyses. The investigators or research assistants responsible for completing the eCRF at each centre will be noted in the investigator's folder.
The investigator is responsible for the accuracy and quality of all the data stored in the eCRF. Data will be verified as they are entered into the eCRF and any modifications to entries will be validated by the investigator; this change will be recorded and justification may also be entered as a comment. Data will be stored at the participating sites for 15 years. An eCRF patient code correspondence table will be made for all patients at both participating centres. It will contain the both the patient's name and eCRF code and will be used in cases of erroneous or missing data. The correspondence table will be held by the principal investigator at each centre in a secure place and no clinical data will be recorded on it.
Confidentiality {27} The study will be conducted in accordance with the recommendations of the French Data Protection Authority and data protection regulations  [25]. All source data will be kept in the investigating centre. The electronic database will be anonymous and will be kept for 15 years according to Good Clinical Practice  [25]. Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33} Not applicable as no biological specimens will be collected as part of this trial.
Composition of the coordinating centre and trial steering committee {5d} The coordination centre is a clinical research unit that provides supports to the study coordinator. It is composed of a project manager, a data analyst and a data monitoring team. Composition of the data monitoring committee, its role and reporting structure {21a} Data monitoring will be carried out by an employee of the sponsor department of the clinical research unit of Vendee Hospital to ensure that the eCRP are completed accurately. Data quality control will be regularly undertaken at each investigating site to ensure that the eCRP are accurately completed. The protocol is considered to be Risk Level A ('low or negligible risk'); it will be monitored to ensure that: -Informed consent has been received from each participant -The clinical centre has complied with the requirements of the protocol -The data are complete and accurate -All original patient information and documentation, such as medical notes, appointment cards or laboratory result sheets, are present and complete.
Determination of the VoLY This determination was directly based on the VoLY equation proposed by Quinet et al  18  in the FGCSP report: VSL ¼ X TðaÞ i¼0 VoLYðaÞ3ð11dÞ 2i (1) where T(a) is the life expectancy at a given age a, d the discount rate, and VoLY(a) is the constant value of a life-year for a given age a, following recommendations by Quinet. This means that for an individual of age a, with a life expectancy T(a), every life-year remaining has the same value VoLY(a). For our study, life expectancy was documented from the French National Statistics Office (Institut National de la Statistique et des Etudes Economiques), which publishes age-specific life expectancy data for the French population annually. We used the most recent data available, which were for 2018. 22 onsidering VSL as a constant, the VoLY can then be estimated: VoLYðaÞ ¼ VSL P TðaÞ i¼0 ð11dÞ 2i (2) In equation 2, given that life expectancy decreases with age and that VSL is constant, the value of VoLY(a) increases with age.
Discussion There is a growing demand for defining reference values for qualifying cost-effectiveness of new products coming to price negotiation in France. Although the issue was first raised in 2012,  12  no such proposal has yet been made. The HAS has conducted a comprehensive literature review exploring methods to measure WTP for a health gain and studies performed to estimate WTP. However, no valid reference values for France were proposed from this review. In this study, we have attempted to provide estimates of VoLY and VSQ that could be used as initial reference values to help qualify ICERs in health technology assessments by the CEESP and in new technology submissions by manufacturers. We have developed a methodology for estimating a VSQ benchmark for cost-effectiveness using only French data (or the specific VSL estimate for France from an international study), which respects the ethical principle of preserving equity of access to healthcare.
Family participation rate was 79%  [4]. An eligible family was defined as comprising at least one parent (>18 years old) who had at least one child under 13 years of age and who spoke one of the 17 languages included in the survey  [4]. Only one parent (preferably the mother) was interviewed by multilingual interviewers using face-to-face standardized questionnaires. A trained nurse examined the children and collected data from their medical records/vaccination cards. For the present substudy, we only included children aged 2 to 13 years whose parents could provide a written record of their vaccinations. No individual electronic vaccination registries were available.
A robust Poisson regression model was used to assess the crude association between each covariate and insufficient VC for each vaccination. Covariate associations with a p value ≤ 0. 15 were selected for the multivariate model. Manual backward stepwise variable selection was used to fit the final multivariate model, which retained all covariates with a statistically significant (p ≤ 0. 05) association with the outcome of interest. The statistical software package STATA 14. 2 (Stata Corp, College Station, Texas, USA) was used for our analysis.
Results Of the 801 children initially sampled, 450 met the inclusion criteria (214 and 236 born in and outside France, respectively). Overall, 52 children were not included due to missing data (country of birth or health information), and another 254 children because they were aged under 2 years. Among the 495 eligible children, 42 were excluded from the analysis because they did not have a vaccination record (7 and 33 born in and outside France, respectively) [Figure  1 ]. The excluded children were more likely to be girls, from European countries, not attending school, and from a low-income family, while they were less likely to be covered by health insurance. Of the 801 children initially sampled, 450 met the inclusion criteria (214 and 236 born in and outside France, respectively). Overall, 52 children were not included due to missing data (country of birth or health information), and another 254 children because they were aged under 2 years.
In France, health-related data (including vaccinations) are systematically gathered for children in maternal and child protection services (at 9 and 24 months of age, respectively), in preschools and primary schools (at 6 and 11 years of age, respectively) and in ninth grade (15 years). This allows the identification of children who are not up to date with their vaccination. Free vaccinations could be proposed as a possible way to improve the existing system in which school doctors do not provide or recommend vaccinations and in which vaccinations are only available at dedicated structures (maternal and child health centers and free vaccination centers, with disparities across the French territory) or in the context of specific interventions (for example, during the recent measles epidemics).
An option being explored is the use of metal bioaccessibility data from alloys and pure ingredients to calculate the relative bioaccessible concentration (RBC) of a metal in an alloy (instead of relying on the bulk concentration) as a more accurate reflection of the properties of alloys. Bioaccessibility is defined as the fraction of a substance that dissolves under surrogate physiological conditions and is potentially available for absorption into systemic circulation. Bioaccessibility is measured in bioelution tests, which measure the fraction of a substance that dissolves under surrogate physiological conditions (e. g. simulated gastric fluid) and is potentially available for absorption. The results of these tests can be used to determine the RBC of a metal ingredient in an alloy, defined here as the concentration of the metal in the alloy that may be available for absorption.
The concept of RBC can be seen as a measure that, when compared to bulk concentration, provides information on a possible "matrix effect" in a mixture, something that is absolutely relevant for alloys, and is the basis for alloying materials. In this paper we present bioaccessibility data collected using bioelution tests in three physiological fluids relevant to the oral and inhalation routes of exposure. The samples tested include pure nickel (Ni) and cobalt (Co) metal samples, as well as a few alloys containing one or both of these metals. Powders and massive forms of Ni, Co, and stainless steel 316L were tested in gastric fluid, while only powder forms were tested in lung fluids. Powder forms of Co-containing alloys were also tested in lung fluids. The RBCs of Ni and Co in the selected alloys were calculated and compared to the bulk concentrations.
Analyses of the samples The ECTX sample analyses were conducted by Waterlaboratorium Noord b. v. using ICP-MS (Inductively Coupled Plasma-Mass Spectrometry), with an accredited reporting limit of 0. 5 μg/L for Ni and Co for gastric fluid and 5 μg/L for Ni and Co for the other fluids. KMHC extracts were analyzed only for Co using ICP-MS (Inductively Coupled Plasma-Mass Spectrometry), with a detection limit of 50 μg/L, and a limit of quantitation of 25. 0 μg/g or 50 μg/g depending on the fluid (some fluids required additional dilutions due to the matrix of the fluid). Information on recovery results and calibration information for sample analyses are included in the Supplemental Material. The data from the analytical laboratories were expressed in μg element per L solution, then recalculated (blank corrected) and expressed in μg element per g test item, percent metal release of the total available element, and released element per surface area (mg/m 2 ) (when possible).
Metal ion release and bioaccessibility of alloys as a surrogate for bioavailability In this study we measured Ni and Co release from alloys in three physiological fluids in order to derive bioaccessibility data relevant to the oral and inhalation routes of exposure. Metal ion release is important because the free metal ion is considered to be responsible for the observed human toxicity. As mentioned in the introduction, alloys do not behave as simple mixtures and therefore the bulk concentration of metals in alloys is not indicative of relative bioavailability or bioaccessibility compared to the pure ingredients. The RBC of a metal ingredient in an alloy, as the concentration of the metal in the matrix that is available for absorption, can be the most relevant measure for estimating the relative toxic potential of an alloy.
Residents who tested positive were either i) treated at their long-term care facilities by local medical staff only, ii) treated at their longterm care facilities in coordination with the AP-HM Home Hospitalisation Unit (HHU), iii) admitted to the IHU (day-care hospital or conventional units), or iv) transferred to AP-MH Intensive Care Units (ICU). For confirmed cases, information on demographics, chronic medical conditions, COVID-19 treatment and clinical data including fever, asthenia, anorexia and weight loss, respiratory symptoms and signs (cough, rhinorrhoea, dyspnoea, chest pain, acute respiratory distress syndrome) and death was collected retrospectively from interviews with the medical team of 24 long-term care facilities and the electronic health recording systems of the AP-HM.
There is still scope to use technology to streamline patient pathways The working group identified four key examples where they felt there were opportunities to streamline patient pathways, but technological solutions in these areas were not yet commonplace: • Antimicrobial stewardship: Up to 50% of antimicrobial therapy given in hospitals is inappropriate. 6 The ability to use existing databases/artificial intelligence to recommend the most appropriate antibiotic, dose and duration for a given patient based on clinical parameters could minimise inappropriate prescribing. This may include tracking global and local antimicrobial resistance patterns, prior antimicrobial therapy, and previous hospitalisations that raise concern for nosocomial cross-transmission. There is also a need for more rapid and accurate diagnostics to increase the proportion of patients who receive a tailored antibiotic treatment as early as possible.
Weighting of the selected criteria Given that criteria can have different relative weights with regard to the overall impact, we used a quantitative tool, and applied the PAPRIKA method  [27]  to determine the weightings in the second part of the study. This method compares pairs of criteria while considering that all the other criteria are identical. Firstly, PAPRIKA software (1000minds Ltd, Dunedin, New Zealand) was used to calculate the number of possible comparisons. Next, based on the number of comparisons and the number of experts, an algorithm was used to define a smaller set of discriminant choices and to mask the implicitly resolved comparisons. Once all the comparisons had been performed, the software calculated the weightings: from the answers, mathematical methods based on linear programming were used to calculate each expert preference values (or 'part-worth utilities'), representing the relative importance (weights) of the criteria to her/him.
Comparisons and weightings of the criteria In view of the large number of criteria and items selected, the PAPRIKA software detected 7,143 possible comparisons. Each expert then performed between 17 and 22 comparisons. The three most strongly weighted criteria were "Mean length of stay in the ED" (weight: 32. 7%), "Time before first analgesic delivery" (18. 2%), and "Waiting time before first treatment" (16. 4%). The weights attributed to each criterion are shown in Fig  2.
This work was funded by  Mundipharma SAS (Paris, France ; www. mundipharma. fr). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. MM received fees for board and conference from  Mundipharma and Purdue. MM also received a grant to conduct a trial on pain management in the ED ( NCT03380247 ). KT received fees for board and conference from  Mundipharma. MLD  received fees from Mundipharma. This work was funded by  Mundipharma SAS (Paris, France ;.
Outcomes The complementarity of the different actions was assessed by the quantitative and qualitative analysis of all interventions traced in the EMR (number of different types of interventions over time, analysis of proposal for each intervention, and impact of interventions on antibiotic use). Antibiotic consumption, defined in daily dose of antibiotics per 1000 patient-days (DDD 1000PD ), was calculated with ConsoRes ® software  [38]  for the years 2016-2019 in the whole hospital. The impact on mean LOS, 30-DRR, and in-hospital mortality was assessed on patients with deep infections from the eight wards (medicine, surgery, and intensive care unit) representing 274 acute care beds that benefited from all the interventions of ASP by comparing two groups of patients over two periods of 18 months: January 2016-May 2017 versus January 2018-May 2019.
Outcomes The complementarity of the different actions was assessed by the quantitative and qualitative analysis of all interventions traced in the EMR (number of different types of interventions over time, analysis of proposal for each intervention, and impact of interventions on antibiotic use). Antibiotic consumption, defined in daily dose of antibiotics per 1000 patient-days (DDD1000PD), was calculated with ConsoRes ® software  [38]  for the years 2016-2019 in the whole hospital. The impact on mean LOS, 30-DRR, and in-hospital mortality was assessed on patients with deep infections from the eight wards (medicine, surgery, and intensive care unit) representing 274 acute care beds that benefited from all the interventions of ASP by comparing two groups of patients over two periods of 18 months: January 2016-May 2017 versus January 2018-May 2019.
Moreover, the PubMed database references articles in the field of health but lacks some in the fields of educational and social sciences. We did not seek to be exhaustive; rather, we sought to provide an overview of the links between definitions of complexity and choices of evaluation methodologies made by researchers who identify themselves in this way. Finally, a review of the scientific literature cannot fully achieve the objectives that we set. A review can deal with the practices of only a small proportion of researchers and stakeholders.
To identify all the subjects who underwent planar V/Q lung scan during their followup as intended, a search by the ICD-10 (International Classification of Diseases, tenth edition) code and keywords was conducted in the Brest University Hospital database and crossed with our cohort study database. All imaging tests were then collected from medical records, and, when available, were printed to establish the RPVO retrospectively. Patients were excluded if they had: (i) an evaluation at 3 to 24 months with tomography V/Q lung scintigraphy, V/Q single photon emission computed tomography (SPECT) or CTPA instead of planar V/Q lung scan; (ii) a history of pneumonectomy; (iii) a diagnosis of recurrent PE at the time of V/Q lung scan assessment at 3 to 24 months; or (iiii) if the imaging was not available for printing.
20 in univariable analysis, (2) a prevalence ≥3%, and (3) a clinical relevance. Discriminant threshold values of continuous variables were established based on clinical relevance and previous studies on RPVO, age and BMI. 9, 14 For IPVO, it was based on the calculation of area under the curve (AUC) of receiver operating curve (ROC) and Youden index calculation. The multivariable analyses were then conducted using a logistic regression model. All tests were two-sided and a p-value less than 0. 05 was considered statistically significant. Statistical analyses were performed using the R-studio software (R Development Core Team (2005). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www. Rproject.
Spikes and force analysis Data were analyzed offline using custom routines in Matlab (MathWorks). First, the position signal was extracted by the video using a white squared marker on the slide as reference. The recording frequency of the video was 25 Hz. After applying an antialiasing FIR lowpass filter, a uniform linear resample of the movement of the slide was performed, in order to synchronize the position signal with the force data, recorded at 100 Hz. To identify the timing of the voluntary activity of the animal, a threshold method was used to detect force peaks during the pulling phase of the task. For the following analysis, we picked out peaks that produced a displacement of the slide, in addition to crossing the threshold; and we calculate the This is a provisional file, not the final typeset article onset of these peaks as the minimum of the force derivative just before the respective peak  (Spalletti et al.
, 2014). The electrophysiological signal, recorded at 40 kHz as sampling rate, was analysed by Offline Sorter (Plexon, Dallas, TX). First, for each channel of the probe, we sorted waveform that crossed a detection threshold of the mean ± 3 standard deviations. Then, detected spikes were clustered using an automatic process based on principal component analysis. Starting from these clusters, a manual sorting was executed to isolate all single units which could be identified in the recorded multi-units signal. The time stamp of each unit was synchronized with the data of the robot. To evaluate the temporal behavior-related spike activity, the peri-stimulus time histograms (PSTHs, NeuroExplorer, Plexon) was generated with bins of 20 ms in an interval of 1 s around the onset of force peaks. In addition, the resting activity of each unit was evaluated selecting intervals of at least 0.
Phase coherence and Functional connectivity Functional connectivity (FC) among cortical regions was inferred from phase coherence of activity measurements, and used to determine changes in brain activity in "stroke" and "rehab" condition, as compared to the healthy mice. These inferred activity changes were used to parameterize simulations of the BNM built over the Allen Brain Atlas mouse connectivity data (http://connectivity. brain-map. org/; (Oh et al. , 2014) , below referred to as the Allen Mouse Brain Atlas -AMBA), incorporated in the extended virtual mouse brain  (Melozzi et al. , 2017). In each animal, the camera field-of-view used for activity measurements was placed in a standard position using the sagittal suture and its intersection with the coronal suture of the skull (bregma) as anatomical landmarks.
To spatially correlate our activity measures with the structural connectivity data  (Oh et al. , 2014) , the camera field-of-view (Fig. 3 , panel B) was spatially translated to the Allen Common Coordinate Framework  (CCF, v3, 2015; (Oh et al. , 2014) ). Since the CCF lacks stereotactic skull landmarks, these were introduced by spatially co-registering all diagrams from a standard stereotaxic mouse brain atlas  (Franklin et al. , 2008)  to the CCF coordinate space with affine transformations defined using the QuickNii tool  (Puchades et al. , 2019). Using bregma and the sagittal suture as a reference, the four corners of the downsampled 128x128 pixels field-of-view of the recorded images were positioned in CCF, taking the 5 degree lateral tilt of the camera view into account. Delineations of layer IV cortical regions were then projected onto the camera field-of-view, and used as a custom atlas reference for all activity maps.