dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants and design The study was based on a longitudinal analysis of data from the SUBstance Users Satisfaction and Quality Of Life cohort (SUBUSQOL). This is a prospective cohort of outpatients who started care for alcohol or/and opioid dependence in French specialized SUD treatment centers (registered in ClinicalTrials. gov ID: NCT02894476). Participants were eligible to participate in the study if they were older than 18 and met the criteria for alcohol dependence, opioid dependence, or both according to the Diagnostic and Statistical Manual, fourth edition (DSM-IV)  (American Psychiatric Association, 2000). Clinicians certified in SUD pathologies who were trained in the use of the DSM-IV recruited patients. The multidisciplinary treatment team included psychiatrists, psychologists, social workers, and specialized nurses.
Data collection We collected sociodemographic and clinical data from medical interviews and clinical testing at the time of enrollment in SUD care (i. e. , the first consultation in the department), which was the time of inclusion in the study. We assessed health-related quality of life (HRQoL), anxiety/depression, and coping strategies with French self-reported questionnaires at the time of inclusion. We assessed satisfaction with a self-reported questionnaire to be completed 15 days after inclusion.
Satisfaction scores Of the 175 patients included, we could use 171 questionnaires. We sent the EQS-C questionnaires 11. 7 days (SD = 4. 2) after study inclusion on average and participants returned in a mean time of 16. 4 days (SD = 19. 5) after they were sent. The completion rate overall by outpatients without applying the process of missing data was 86. 5%. The satisfaction scores are shown in Table  3. The mean total satisfaction score was 79 (SD = 15. 6). The mean satisfaction scores were 79. 4 (SD = 18. 8) for "contact/appointments", 78. 1 (SD = 16. 2) for "reception facilities", 73. 0 (SD = 24. 6) for "waiting time", and 80. 8 (SD = 17. 2) for "consultation with the doctor".
Data We used two nationally representative sources of data. For measuring patient experience and health status, we used the national General Practitioner Patient Survey (GPPS), a postal survey administered to a sample of registered patients from all GP practices in England annually, which has been conducted twice a year from 2012 until 2016 (annually from 2017). 13 To assess health care costs, we used the Hospital Episode Statistics (HES) database for England (from April 2009 to March 2018). 14 This database includes administrative data recording all patient contacts with National Health Service (NHS) hospitals. Using individual-level data from each source, we created a dataset where each observation represents one of eight segments of each GP practice (combinations of presence of multimorbidity, aged over 65 years, and gender) at each time point (by survey wave for GPPS and annually for HES).
Outcome measures For our primary analysis, we measured effectiveness on three outcomes: (1) patient experience, using the question 'In the last 6 months, have you had enough support from local services or organisations to help you to manage your long-term health condition(s)?' (GPPS). We analysed the proportion of those with long-term conditions answering, "Yes, definitely"); (2) generic health status (health-related quality of life) as measured by the EQ-5D 5 L index (GPPS),  15  a continuous measure with 0 equivalent to death and 1 equivalent to perfect health; (3) health care costs, using the total costs of secondary care per registered person per year as calculated by tariff costs (a set of nationally set prices) of each contact,  16  that is admission, outpatient visit, and emergency department attendance as recorded in the HES database.
Analysis We analysed the data using a quasi-experimental difference-in-differences design. We compared GP practices within Salford and South Somerset Vanguard sites (intervention) to control ('usual care') practices, using two comparators (excluding practices in other Vanguard sites): (1) all other GP practices in the rest of England; and (2) NHS Rightcare peers. Rightcare peers are the 10 most similar geographical areas, defined by NHS England on the basis of 12 demographic variables. 18 Identification of the intervention's causal effect using difference-in-differences analysis assumes that there are parallel trends in outcomes if the intervention was not implemented. This assumption is not testable over the entire analysis period since the intervention has been implemented. However, we assessed the plausibility of this parallel-trends assumption by examining the interaction of intervention status and continuous time in the pre-intervention period.
19 e employed propensity score weighting proposed by Stuart et al. to ensure comparability of intervention and control units. 20 e defined the intervention start date as the date when two sites received their first set of Vanguard funding in April 2015, by which date the sites began implementing the respective care model. We used all data available. For GPPS we used three pre-period years (July 2012-March 2015, six survey waves) and two years follow-up (July 2015-April 2017, four survey waves). For HES data we used six pre-period years (April 2009-March 2015) and three years follow-up (April 2015-March 2018). We first report the overall post-period effect before decomposing the effect by year to observe any variation in the effects over time. It is possible that effects might be diluted at the population level.
Results in context Our findings are broadly in line with previous evaluations of interventions that targeted populations at high risk of hospitalisation in the UK and internationally. 2] [23]  A recent analysis of the South Somerset Vanguard programme was able to study the effects of each individual service delivery intervention using data on directly treated individuals. Similar to our study, that work found an increase in the total costs of primary, community and hospital care while there was no statistically significant effect of (prevention-oriented) enhanced primary care on service utilization. 24 An analysis of a pooled sample of all 23 populationbased Vanguard models also found no significant reduction in emergency admissions over three years in total, although there was some evidence of a relative net reduction compared to controls in the third year.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This project (SELFIE) has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  634288. The content of this presentation reflects only the SELFIE groups' views and the  European Commission  is not liable for any use that may be made of the information contained herein. Jonathan Stokes  was additionally supported by an  MRC  Fellowship. Søren Rud Kristensen  was additionally supported by the  NIHR Imperial Patient Safety Translational Research Centre.
| METHODS This was a multicentre, prospective, randomised controlled clinical trial designed to evaluate the preliminary effectiveness and safety associated with the use of ASCS for the treatment of chronic leg ulcers associated with venous insufficiency. The clinical benefit of the addition of ASCS treatment to standard practice (debridement and cleansing) and multi-layer compression bandages (Profore, Smith and Nephew, London, UK) was compared with standard practice (debridement and cleansing) and multilayer compression bandages (Profore) without ASCS treatment. This study was registered at clinicaltrials. gov under number: NCT01743053. Subjects with an open leg ulceration were recruited from six sites in England and one site in France between July 2013 and November 2015. Ulcers were to have been present for at least 4 weeks and present with an area of.
Independent analyses were conducted by FGK Clinical Research, Munich, Germany. All statistical analyses were performed using SAS 9. 3 TS Level 1M1 (SAS Institute Inc. , Cary, NC). A P <. 05 was considered significant. Incidence of healing, ulcer duration, smoking, and comorbidities were analysed by Fisher's exact test. Subject age, body-mass index (BMI), and wound duration were evaluated using the Wilcoxon Test (Two-sided). Gender was evaluated using the Pearson Chi-Square test. Time to first closure was calculated using Kaplan Meier survival analysis and Cox regression. Wound size, pain and HRQoL scores for the treatment and control groups were compared using an analysis of variance (ANOVA). Data analyses were stratified according to initial ulcer size.
When evaluating the incidence of closure at Week 12 for the PP analysis population, a larger difference exists between ulcers treated with ASCS + compression (38. 5%) versus compression alone (15. 0%) than occurred for the FAS; however, these data should be cautiously interpreted because of the small sample size evaluated. In addition to incidence of closure at Week 12, time to first closure was calculated using Kaplan Meier survival analysis and Cox regression. For Group 2, the mean time to first ulcer closure was 43. 0 days in the ASCS + compression group versus 84. 0 days in the compression only group; however, no significant difference in time to first ulcer closure was observed, likely because of the small sample size. The mean absolute change in wound area from baseline was evaluated using the Eykona Wound Measurement System.
Data collection After an initial training with a senior lecturer (LF) who is expert in qualitative research, one of the authors (HL) conducted the interviews. She introduced herself as a healthcare student. All interviews were digitally recorded and transcribed verbatim. Field notes were made throughout. Written informed consent was obtained from each interviewee before the interview for all the information used for the study purposes. The interview guide was developed based on the literature review performed at the beginning of the study (EA, HL), and after discussion with a DSME expert (AC) and all co-authors (AM, CK, LF). The final version was pilot tested with patients with T2DM from another primary care practice. The interview guide included three sections: 1) description of their type 2 diabetes history (how it started, how it has changed over time); 2) reasons for participating in the DSME programme, and the obtained benefits; and 3) factors that motivated their attendance.
Data analysis All recorded interviews and field notes were transcribed verbatim using the Microsoft Office Word software (HL) and were not returned to participants for comments or corrections. The thematic analysis was conducted concomitantly with the interviews to answer the research question. Two researchers (EA, HL) independently coded these interviews in an active and reflexive process as defined by Braun and Clarke  [25]. After familiarising with the interview content, each researcher independently generated the initial codes of the interview. Each researcher compared the analysis of the last interview with that of the previous interviews in an iterative approach. These codes were then discussed by the two researchers, interpreted, and grouped in themes. In the case of conflict, a third researcher (LF) independently analysed the interview, and contributed to the discussion.
Their barriers were integrating the DSME programme in their own agenda, self-expression and keeping the motivation in the long-term. We did not collect any data among non-attendees. Therefore, we do not know anything about the reasons of non-attendance and of refusing the interview by some people who attended the programme. However, in a systematic review in 2017, Horigan et al. showed that the difficulties listed by people who did not attend a DSME programme were similar to those experienced by people who attended and presented these barriers in two categories: 'could not attend' and 'would not attend'  [8]. Nevertheless, a study which will focus on non-attendees could bring valuable information that could be used to improve attendance.
Data analysis 2. 2. 1. Fraction in the organic layer The fraction of radiocesium retained in the organic layer was calculated to describe the time series of migration of 137 Cs activity inventories from organic layers to mineral soil layers. The ratio of the radiocesium activity inventory in the organic layer to the sum of that in the organic layer and mineral soil layers is given by: F l/t = I l I l + I s × 100 (%) (1) where F l/t is the fraction in the organic layer, I l is the inventory in the organic layer and I s is the inventory in the mineral soil layer.
However, this is because 137 Cs inventory is higher in the organic layer than in surface mineral soil layers at many sites (76%) in  Muto et al. (2019)  similar to European forests. We used three different parameters to quantify and summarise vertical migration of radiocesium in forest soils. However, values of X c and α were influenced by the local sampling designs. Specifically, the depth of the soil profiles sampled and soil sampling intervals both affect estimates X c and α. In addition, this research did not contain uncertainties for sampling and analysis for each reviewed data set because this information was not reported in the original references. Therefore, although we could not consider uncertainties to our analyses, we need to be cautious that the results of this parameter include these uncertainties.
Acknowledgment This study was supported by the  Forestry Agency, Japan. We thank the village office of Kawauchi for their cooperation. This study was revised throughout the discussions with members of the  IAEA MODARIA II  programme, Working Group 4 (Transfer Processes and Data for Radiological Impact Assessment), subgroup 2 on Fukushima Parameters. We thank  Dr. Brenda Howard ,  Dr. David Howard ,  Dr. Alexander Ulanowski ,  Dr. Gerhard Pröehl ,  Dr. Keiko Tagami  and  Dr. Sergey Fesenko. This work was supported by  JSPS KAKENHI  (Grant Number  JP16H04945  and  JP20K15563 ). YT received the financial support provided by the  French Agence Nationale de la Recherche (ANR) , allocated in the " Investissements d'Avenir" framework programme  under reference  ANR-11-23 RSNR-0002.
3) Of the 884 residents and workers believed to be present within a 100-m radius of the attacks when they occurred, 259 (29%) responded to the Fig. 1  Process for inclusion of participants in the IMPACTS survey according to the different data sources used in the survey invitation letters. Of these, 83 (32%) were excluded because they did not meet the inclusion criteria. Of the 176 who were eligible, 75 (43%) declined to participate in IMPACTS, while 101 agreed (agreement rate: 57%). 4) Twenty-three people were identified as close relatives of people involved using snowball sampling. Of these 1 did not meet the inclusion criteria. Five of the remaining 22 (23%) declined to participate in IMPACTS while 17 agreed (agreement rate: 77%). To summarize, in Wave 1, of the 489 people who responded to the inclusion questionnaire, 99 did not meet the inclusion criteria (20%).
Of the remaining eligible 390, 105 declined to participate (27%) while 285 agreed they would participate in the longitudinal study (global agreement rate to participate in IMPACTS: 73%). However, of the 285 who initially agreed to participate, 190 participated in Wave 1, 47 only participated in Wave 2, 45 did not in the end participate in either wave, and 3 were secondarily excluded because of inaccurate reporting (Fig. 2 ). The actual participation rate in Wave 1 was therefore 49% (190/390). Among the 105 persons who declined to participate, the reasons cited were a desire not to be reminded of the painful event (32%, n = 34), lack of time (16%, n = 17), not feeling concerned (15%, n = 16) and the fear of data leakage (5%, n = 5). Thirty-three people (31%) did not provide any reason for their refusal.
Notably, we suggest i) to make efforts to collect information of non-participants in an ethically respectful way, ii) to use an open cohort design in longitudinal post-terror studies in order to allow for later participation among survivors who are unable to participate in the first wave during the early aftermath of the attack, and iii) to systematically estimate levels of and factors associated with non-participation and attrition to take into account potential selection bias in the interpretation of the findings. revision of the manuscript. PC contributed to survey design and the data collection of Wave 2 of the IMPACTS survey, and supervised the writing of the paper. SV designed, supervised the questionnaire conceptualization and the data collection of the two waves of the IMPACTS survey, and supervised the data analysis, the interpretation of the analysis and the writing of the manuscript.
Acknowledgements The authors are most grateful to all the study participants for their involvement especially given the very difficult context for them, and to all the interviewers. The authors would like to acknowledge the support from the national French agency for public health as well as the regional agency for health of Paris area:  Anne Gallay ,  Thierry Cardoso ,  Anne Laporte ,  Jean Claude Desenclos ,  Clothilde Hachin ,  Naine Isserlis ,  Michel Gentile ,  Laurent Kosorotoff ,  Martial Mettendorf ,  Claude Evin , and  François Bourdillon  for generously contribution their time and energy to the conduct of IMPACTS study. A special thanks to  Laurent Bernard-Brunel ,  Alexandra Botero ,  Jean-Michel Coq ,  Nathalie Cholin ,  Nicolas Dantchev ,  Elise Neff ,  Marc Grohens ,  Aurelia Rochedreux ,  Toufik Selma ,  Laure Zeltner  and  Julien Sonnesi  for their field investigation.
16S rRNA Gene Sequencing and Sequence Analysis Fecal DNA was extracted using the Quick-DNA Fecal/Soil Microbe 96 Kit (ZymoResearch, Irvine, CA), and the 16S rRNA gene V3-V4 region was amplified by PCR and sequenced by MiSeq Illumina Sequencing as previously described. 24 Sequencing reads were deposited in the National Center for Biotechnology Information Sequence Read Archive (SRA accession: PRJNA591810). 16S rDNA amplicon sequences were analyzed using the FROGS pipeline (version 3. 0)    according to standard operating procedures. 25 Amplicons were filtered according to their size (400-500 nucleotides) and clustered into OTUs using Swarm (aggregation distance: d = 1 + d = 3). After chimera removal, OTUs were kept when representing more than 0. 005% of the total number of sequences. 26 OTUs were classified using the reference database silva138 16S with a pintail quality of 100.
Statistical Analysis All statistical analyses were performed using the R software (version 3. 5. 1). The microbiota composition analysis was performed using the phyloseq package (version 1. 26. 1). 29 For α and β diversity analyses, the samples were rarefied to even sequencing depth (9694 reads per sample). Richness (observed OTUs) and Shannon α-diversity were calculated. The βdiversity was analyzed using the Bray-Curtis distance and plotted by non-metric dimensional scaling (nMDS). Permutational multivariate analysis of variance (PERMANOVA) was used to test the effects of day, maternity, and their interaction on the Bray-Curtis distance between samples by using the vegan package (version 2. 5-6). OTUs representing less than 0. 05% of the total number of sequences were filtered out. OTU unrarefied counts were agglomerated at the phylum, family, or genus level, and relative abundances were calculated at each taxonomic level.
Heatmaps and clustering analysis (average method) were performed with the made4 package (version 1. 56. 0). All univariate analyses were performed with a linear mixed model (lme4 package version 1. 1-21) with the fixed effects of day (day 13 or 23), maternity (maternity 1 or 2), and their interaction, while the piglet and sow were used as random effects. Analysis of variance tables was performed with the car package (version 3. 0-6). When the interaction between day and maternity was significant, the means of each group were compared pairwise with the emmeans package (version 1. 4. 5) with the Tukey correction. Spearman correlations were calculated between the relative concentrations of metabolites and the relative abundances of bacterial families or predicted pathways. P-values obtained from analysis of variance and correlation were adjusted for multiple testing with the false discovery rate Benjamini-Hochberg procedure.
Model Development OpenFOAM is used as the modelling tool. It is a Computational Fluid Dynamics (CFD) open source software based on C++ programming language for the development of numerical solvers in continuum mechanics. Finite element method is employed to solve continuity and general transport equations in order to calculate the vapour intrusion. The 3-D model proposed in this work simulates the vapour transport in the vadose zone and its entry into the building through the vapour intrusion pathways (foundation porosity and existing cracks). This model is based on the following hypotheses: • Stationary pollutant transport. • Soil considered as a homogeneous and isotropic porous media. • Vapour source constant and infinite. • No transfer towards the groundwater allowed. • Pollutant entering into the building mixes immediately and homogeneously  [4].
Enough information should be provided to indicate that a formal synthesis is planned (whether that be quantitative, qualitative or formal narrative synthesis). You should include details for your specific review. Strategy for data synthesis: field #28 Give the planned general approach to synthesis, e. g. whether aggregate or individual participant data will be used and whether a quantitative or narrative (descriptive) synthesis is planned. Mention software, tools and statistical tests you plan to use. Include the number of researchers involved and how discrepancies will be resolved. You need to be more clear in the type of synthesis you wish to perform, as the current information is a bit vague. You have also ticked in field #30 to state that a meta-analysis will be conducted, yet there is no mention of this in field #28 -please clarify whether or not this will be produced, and if so, how? The record has been referred to you.
Study design We included all consecutive patients admitted to the thoracic oncology unit (conventional hospital stay) of the Grenoble University Hospital, France, between 28 th January 2014 and 31 st March 2016, and followed them up until 31 st December 2016. Data were collected prospectively. Only patients with thoracic tumours like lung cancer and mesothelioma were studied, regardless of their ECOG-performance status (PS). Exclusion criteria were absence of cytological or histological cancer diagnosis, as well as remission for more than 5 years. At hospital admission, each patient benefited from a standardized advance care planning developed at the Grenoble University Hospital (annex 1). The programme was established based on the legal framework of the Kouchner Act of 4 th March 2002, Leonetti Act of 22 nd April 2005, and Leonetti-Claeys Act of 2 nd February 2016  [12] [13] [14].
Data collection The following patients' data were recorded: ECOG-PS, weight loss, Charlson Comorbidity Index (CCI)  [16] , and cancer history (status, histological type, and previous and current anticancer treatments). Reasons of admission were likewise collected, as were causes of organ failure. The advance care planning performed at admission and upon subsequent hospitalisations was recorded, as was the manner how planning evolved (more or less intensive), along with the respective reasons (annex 1). Care intensity propositions were divided into three categories; 1) intensive care (cardio-pulmonary resuscitation, transfer to the ICU, oro-tracheal intubation or tracheotomy, dialysis, and inotrope administration); 2) maximal medical care (non-invasive ventilation, artificial nutrition, antibiotic treatment, and blood transfusion); 3) exclusive palliative care (peripheral venous catheter, complementary examinations, vital signs, anticoagulation, fibro-aspiration).