dataesr/scientific-paragraphs-categorization huggingface dataset:
Literature review The first stage involves a literature analysis to contribute to the elaboration of the initial middle-range theories. A scoping review will be conducted to identify strategies applying harm and risk reduction strategies with alcohol users. It will give us an overview of intervention strategies used in this approach as well as of potential effective levers. Our research objectives correspond to the following indications of the scoping review: to identify the types of evidence available in a given field, clarify key concepts/definitions in the literature, examine how research is conducted on a certain topic or field, identify key characteristics or factors related to a concept and identify and analyse knowledge gaps. 37 We will conduct our search via bibliographic databases: MedLine using the PubMed portal, PsycInfo, PsycArticles, Behavioural Sciences Collection and Web of Sciences.
The keywords will be 'harm reduction', 'manage alcohol', 'intervention', 'addiction' and 'alcohol'. The inclusion criteria are: English or French language publications from 2011 to 2021; original articles focusing on the evaluation or exploration of HR intervention strategies with alcohol users. Using the Covidence software, two researchers will conduct a double-blind review of the selected reports and extracted information regarding the intervention (name, location, population, design), evaluation design (method), result of the intervention, its key functions and context of implementation. The purpose of this review will be to extract information about the contexts, mechanisms and outcomes that are the focus of our study. Our analysis questions are: what are the most effective intervention techniques? What contextual conditions influence the impact of the techniques? What are the conditions for the success of these interventions? For which populations? The results will be used for the initial development of middle-range theories.
The University of Bordeaux has ensured that all the regulatory procedures related to the ECIAE study have been carried out. The methods' development, data collection and analysis will take the following issues into account: ► Anonymity of study respondents will be preserved and ensured at all times. Unnecessary collection of personal data will be avoided, and respondents will have the right to review outputs and withdraw consent. All personal data will be coded, removed from the data for analysis and stored separately. Only designated research staff will have access to the keys linking data with personal information. ► Information regarding the study and the right to refuse to participate will be distributed to all study participants. , In the case of refusal, alternative means of data collection will be explored (eg, alternative respondents).
Aims: To estimate the number of people with diabetes (PWD) requiring emergency services for severe hypoglycemia (SH) in France and the associated costs. Methods: PWD were identified using the National Health Insurance database (SNDS). Type of diabetes was determined by the medications used. Matching of the emergency department (ED) visits database (RPU) and the SNDS was used to identify ED visits for SH in 2016 and 2017. SH was identified in the RPU using specific ICD-10 codes. The main SH-related costs analyzed were costs of ED visits, hospitalization, and transport. Results: Of 3,236,600 PWD in 2017, 781,084 had at least one ED visit (11. 5% type 1 diabetes [T1D]) and 9,160 patients had at least one ED visit for SH (40. 2% T1D; 55. 9% male; mean age 63. 0 years; mean diabetes duration 17.
Introduction Diabetes mellitus is a well-known major public challenge. In 2017, an estimated 3. 2 million patients in France affiliated to the General health insurance scheme or local mutual schemes (Sections Locales Mutualistes [SLM]) had medical care for diabetes on the basis of medical claims algorithms from the French national medical insurance system database (Syst eme National des Donn ees de Sant e [SNDS])  [1]. Insulin and other glucose-lowering medications may cause hypoglycemia. Severe hypoglycemia (SH), defined as an event characterized by cognitive impairment that requires assistance of another person  [2] , may require emergency medical assistance including mobile emergency services, ambulances, and visit to an emergency department (ED)  [2]. While the French national health insurance (NHI) has reported the total costs of diabetes in France, to our knowledge, the complete economic burden of SH linked to the use of emergency services has not yet been described.
Some observational studies have estimated the costs of SH in people with diabetes (PWD) and the utilization of emergency services in France; however, these are based on regional patient samples or declarative surveys with potential information bias  [3, 4]. Previous studies considered the overall direct healthcare costs of diabetes in France in type 2 diabetes (T2D) only  [5] , or focused on the direct costs of hospitalization only for SH and do not consider various scenarios, including patients attending EDs who immediately leave the hospital or patients managed by mobile emergency services  [6]. To our knowledge, there are no published studies based on the entire French population and focusing on the utilization of emergency services for SH in PWD. In the absence of a register providing detailed information on diabetes, the medico-administrative databases constitute a valuable alternative source of information.
Study design This was a retrospective study using data from PWD affiliated to the French General health insurance or SLM schemes requiring emergency services for SH identified through the ED visits database (R esum e de Passage aux Urgences [RPU]) and the NHI databases (SNDS) in 2016 and 2017. The study was carried out over two years to verify the robustness and consistency of data although this report focuses on 2017. The study included patients from the time of emergency service entry until emergency service discharge or hospital discharge (if applicable). In accordance with French regulations, the study protocol was approved by the ethics and scientific committee for health research, studies and evaluations (CEREES) and by the French data privacy committee (CNIL).
Data sources This study utilized the medico-administrative data of the SNDS based on PWD affiliated to the General health insurance or SLM schemes, which covers around 87% of people with insurance in France. Data were extracted from SNDS databases that contain individual billing and reimbursement of outpatient healthcare consumption data (Donn ees de Consommation InterR egimes  [DCIR] ) and the national hospital discharge database (Programme de M edicalisation des Syst emes d'Information [PMSI])  [9] [10] [11] [12]. The DCIR and PMSI are connected by means of a unique anonymous identifier encrypted twice in the SNDS. The SNDS contains comprehensive individualized, anonymous data on healthcare expenditures reimbursed by the NHI. The DCIR includes community care and private hospital healthcare consumption at the individual level.
Although the DCIR does not contain the reason for healthcare use, it contains information regarding longterm disease (Affections de Longue Dur ee [ALD]), an administrative category based on the ICD-10; related healthcare consumption; and sick leave. The PMSI includes data about hospital stays in conventional medical units, rehabilitation units, homecare units, and psychiatric institutions, and includes medical data for each stay and information about discharge diagnoses. ED visits are captured in the SNDS database; however, the reason is not provided. This information is registered only in the RPU database which includes medico-administrative data about patients attending EDs  [12]. Providing RPU data by public or private health establishments with an ED has been mandatory since July 2013.
Matching with RPU Unlike the DCIR and PMSI, which are connected by a unique patient identifier, the RPU is not linked with the SNDS and, therefore, an indirect matching was required to identify the ED visits for SH from the two databases. Matching between the SNDS and RPU was carried out using the following variables: (1) patient age at ED visit, (2) gender, (3) establishment and date of ED visit or hospitalization, and (4) geographic code of residence. The hospitalization information technical agency (Agence Technique de l'Information sur l'Hospitalisation [ATIH]) provided the RPU database to the French national insurance organization (Caisse Nationale de l'Assurance Maladie  [CNAM] ) and the matching between data sources was performed by CNAM.
Study population This study consisted of PWD affiliated to the General health insurance or SLM schemes who required emergency services for SH in 2016 and/or 2017. Diabetes was identified using the most exhaustive algorithm published by CNAM in the mapping of diseases based on the dispensing of antidiabetic medication/insulin, and/or if patients had long-term disease (per ALD) and/or hospitalization relating to diabetes-associated ICD-10 codes  [7]. The test performances of algorithms to identify known diabetes were assessed using data from the CONSTANCES cohort, constituting a large representative sample of the French adult population. The algorithm used in this study, which combined ALD, treatment reimbursement, and hospitalizations showed high test performance  [14]. There is currently no published algorithm to differentiate type 1 diabetes (T1D) and T2D based on the SNDS.
Statistical analyses Descriptive analyses were performed. Continuous variables are summarized with: number of patients (N) (without missing values), mean, SD, median, minimum and maximum, first and third quartiles. Continuous variables are presented as mean (SD), unless otherwise specified. Categorical variables are summarized as percentage of patients per category. The denominator for percentage calculations was based on the number of observed data unless otherwise specified. Statistical analyses were performed with SAS software (v9. 4). Analyses were performed separately for 2016 and 2017, for the total population and by subgroups of patients according to age (<15 or ≥15 years) or type of diabetes (T1D or T2D). PWD with ED visits for SH and the numbers of visits were quantified. Patients with several ED visits in the same year were counted once and described.
Missing data Missing data are expected due to unmatching between the RPU and SNDS or missing data in the RPU. Three types of missing data were considered: (1) diagnosis of unmatched ED visits with no hospital admission; (2) diagnosis of matched ED visits without hospital admission if the diagnosis is not registered in the RPU; (3) information about SMUR or firemen, either because individuals were unmatched or information was missing. A predictive model was used to impute missing information: six logistic regressions were used to predict the probability of the ED visits being due to SH, and the probability of arrival by firemen or arrival by SMUR for (1) unmatched visits and (2) matched visits but missing data for the visit. Details of the predictive model are provided in Supplementary Appendix 3.
Study population In 2017, 3236,600 PWD were reported in the SNDS. Of these, 781,084 (24. 1%) had at least one ED visit with a total of 1241,190 visits. Of PWD with an ED visit, 660,576 patients (84. 6%) had T2D, with 32. 4% of these having an insulin prescription, and 89,727 patients (11. 5%) had T1D (Fig. 2 ); 5. 8% had a glucagon dispensing within the last 2 years. There were 445,301 ED visits without hospital stay (66%) matched with the RPU. Only 23,798 ED visits with hospital stay (5%) were matched with RPU (Fig. 2 ). As the reason for ED visit is also reported in the PMSI for ED visits leading to hospitalization, PMSI data was used for these visits. There were 9160 patients (9985 visits) identified as having at least one ED visit (with or without hospitalization) or hospitalization in an ICU for SH.
After imputation of missing data using a predictive model, the incidence rate was estimated to be 0. 33 ED visits for SH/ 100 patients with diabetes/year. In Hypo15, a French multicentric study performed by nine emergency units, the number of SH was estimated to be 0. 57/100 patients/year  [3]. In Hypo15, the incidence of SH events was estimated based on hypoglycemia that motivated a call to SAMU center 15 (emergency call center) and led to simple regulation or an intervention  [3]. In the current study we considered the number of patients who visited emergency services only, which could explain the lower incidence observed. Although the SNDS is not a clinical database, this study nevertheless provides interesting information concerning the profile of patients attending emergency services for SH.
The main strength of this study is that it captured almost all French PWD using SNDS database matching with RPU. The results cover a large part of the French population, around 87% of people with health insurance  [12]. In comparison, previous studies on diabetes using French NHI databases were performed on smaller, often regional, samples. Selection bias in patient participation is limited because the dataset is quasi exhaustive, and data are routinely collected for reimbursement purposes and not specifically for this study. However, despite covering the majority of the French population, specific schemes are not covered (including the agricultural scheme and selfemployed workers)  [12]. To our best knowledge, no exhaustive data are available to date to state whether these populations have characteristics different from the population benefiting from the General Scheme.
The main objective of our study was to better understand the management of SH occurring in an outpatient setting and requiring emergency services. Previous studies evaluated SH in a hospital setting and, therefore, were not designed to distinguish the place of occurrence of SH (in outpatient setting or in patients already hospitalized)  [6]. To overcome these limitations, we matched the SNDS database with the RPU database to consider only events that occurred in outpatient settings and requiring emergency services (with or without hospitalization). Reporting of RPU data by hospitals has been mandatory since 2013. The number of hospitals that report RPU data (67% in 2014 versus 90% in 2017 and 94% in 2018) and the data exhaustivity (70% with valid diagnosis in 2014 versus 76% in 2017 and 77% in 2018) have improved over time  [13, 22].
In this study, 66% of ED visits without hospitalization were matched with the RPU but only 5% of ED visits leading to hospitalization were matched because RPU reporting is not mandatory for hospital invoicing. Therefore, we used diagnosis reported in SNDS for these visits. Moreover, a diagnosis was reported in the RPU for 74% of matched ED visits. Imputation was performed to predict SH for visits with missing diagnosis. Data after imputation of missing data confirmed patient characteristics and costs but did not alter the study conclusions. Transport to emergency services by SMUR and firemen was not reported in the SNDS but was reported in the RPU; however, this information was recorded for only 44% of visits (matched and unmatched). Contrastingly, in an ATIH report on hospital activity in 2018, transport was well reported in the RPU (81%)  [13].
Methods The ESICM UNITE-COVID study was a multicenter, international, anonymized point prevalence study. An international steering committee of experts was established in 2020 by the ESICM. A network of national coordinators recruited investigators, coordinated study participation, and monitored local ethics committee approval at each participating center in the individual countries. The Ghent University Hospital Ethics Committee approved the study (registration number BC07826). The study was not funded, and participation was voluntary. The trial was registered at ClinicalTrials. gov (NCT04836065). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting of observational were followed throughout this manuscript  [7].
Variables The data collected included the demographic characteristics of the patients, their comorbidities, duration of COVID-19 symptoms, clinical status at the time of admission to the ICU, complications during ICU stay, drugs used as part of routine care or because of participation in a research study (including antimicrobials, sedatives, neuromuscular blockers, anticoagulation, and anti-inflammatory therapies), as well as any organ support during ICU stay. Patient data were collected from the day of ICU admission until day 60 following admission to the ICU. Center data were collected separately and included hospital type and unit characteristics, number of beds before the COVID-19 pandemic and on the day with the highest number of COVID-19 patients under intensivist care, and ICU beds in non-ICU locations that were available and managed by the ICU team.
Data sources and curation The requested information was collected in a structured format. All data were submitted by the participating centers through a secure cloud-based electronic Data Capture platform (Clinfile, Vélizy-Villacoublay, France). A comprehensive data curation exercise was undertaken and Data Acquisition, Quality and Curation for Observational Research Designs (DAQCORD) checklist is included in the supplementary materials  [8]. Curation scripts and curated data dictionary which include data missingness are available on GitHub and archived  [9]. To address information bias, we performed several subanalyses, and considered missingness both at patient and at unit level. There was no apparent bias for considered outcomes due to loss to follow up. Bias related to heterogeneity between centers (differences in data reporting and in medical practice) were considered using random effect model at unit level, and variation between centers is reported in the results.
05 as exclusion threshold)  [10]. The final model was built with variables retained from previous steps. Model performance was calculated on the test cohort after excluding collinearity and singularity and again on the full cohort after multiple imputation for missing data. Other methods on model development and testing, on multiple imputation procedure and database curation are reported in the supplemental material. A sensitivity analysis was conducted including only patients on invasive ventilation at ICU admission and admitted for respiratory failure due to COVID-19. Statistical analyses were all performed using R Statistical Software (R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, version 4. 1. 1).
Demographics and baseline comorbidities A total of 4994 patients were included in the study. Baseline data and comorbidities are reported in Table  1. The majority of patients (71. 4%, IQR across units 60%-86%) were male and the median age was 62 years (IQR 53-70). 5. 6% of the patients were healthcare workers, and 2. 5% of female patients were pregnant. The most common comorbidities included arterial hypertension, chronic cardiac disease, chronic pulmonary disease, and asthma. The median BMI was 27. 9  (25. 3-32. 3); 37. 3% of the patients were obese, while 39. 1% were overweight. The rates of most baseline comorbidities were similar in patients on invasive mechanical ventilation (IMV) at the time of admission and in patients not on IMV at admission but requiring IMV at a later stage. The exceptions to this rule were diabetes and chronic liver disease which were more common in the latter.
We could not include all variables and related details including exact indications for organ support which could have improved the analysis, due to the difficulties in completing data collection at the end of the first wave of COVID-19 patients. We relied on the data entered in the clinical notes, and it is possible that not all events were recorded, and the prevalence of adverse events may even be higher. Finally, our data refer to the first wave, and conclusions may not apply to subsequent waves. However, our data represent one of the largest populations of critically ill COVID-19 patients, with data collected on the peak day of COVID-19 admissions when the strain was the highest. Also, we focused not only on the clinical effects and management of COVID-19, but also on organizational changes in response to the steep increase of critically ill patients.
Acknowledgments: The authors would like to thank  Alain Prochiantz  for constant support. This work received support under the program " Investissements d'Avenir" launched  by the  French Government  and implemented by the  ANR , with the following references:  ANR-10-LABX-54 MEMO LIFE -ANR-11-IDEX-0001-02 PSL *  Research University.
As both DM4 and MeDM4 were observed as circulating entities after administration of previous ADC of the same construct (SAR3419: a mAb-SPDB-DM4 ADC  [18] ) they were hence quantified following SAR408701 administration. Based on promising preclinical data presented by Decary et al. [19] , SAR408701 appeared to be a promising candidate for clinical development. It is therefore currently tested in patients with advanced solid tumors expressing CEACAM5. SAR408701 is administered intravenously as a conjugated antibody containing species with different payload densities. The number of cytotoxic molecules per antibody is defined as the drug-to-antibody ratio (DAR). DAR distribution is heterogeneous and ranges from 0 to 8, with an average DAR in the administered solution of 3. 8. During TED13751 first in human (FIH) clinical study (ClinicalTrials.
gov Identifier: NCT02187848), SAR408701 (i. e. , conjugated antibody with at least one payload: DARC1), unconjugated DM4 and MeDM4 were measured in plasma. While total antibody (i. e. , conjugated antibody and naked antibody: DARC0) is usually measured during ADCs development, an innovative approach  [20]  allowed specific quantification of naked antibody (NAB) in TED13751 study. Moreover, new assays have also recently been developed  [21, 22]  to measure individual DAR moieties. DAR was therefore characterized in a subset of TED13751 patients and expressed as average DAR and proportions of individual DAR species over time. Our objective was to elaborate a model framework that investigates SAR408701 disposition mechanisms, to support its ongoing clinical development. This model aimed to integrate ADC prior knowledge by fitting simultaneously PK data of SAR408701, NAB, DM4 and MeDM4, including DAR measurements in cancer patients.
Population PK modeling Population PK model included PK data of SAR408701, NAB, DM4, MeDM4, average DAR and proportions of individual DAR species over time. As all entities were fitted simultaneously, DM4, MeDM4 and NAB concentrations were converted to ADC molar equivalent (normalization by SAR408701 molecular mass, Table  2 ). PK parameters were assumed to follow a log-normal distribution and interindividual variability (IIV) was estimated on most of the parameters as follow: P i ¼ P TV Â exp g P;i À Á Where P i is the individual patient parameter, P TV the population parameter typical value and g P;i the individual random effect for this parameter, normally distributed with a variance x p 2. Residual variability was modelled with combined error model for SAR408701, proportional error model for DM4, MeDM4 and NAB, and additive error model for DAR as follow: Obs i ¼ f i þ a i þ b i Â f i ð Þ :r i Where Obs i is the individual observation for a given entity, f i the predicted concentration for this entity, r i a random effect with normal distribution, zero mean and unity standard deviation, a i the additive error term and b i the proportional error term.