dataesr/scientific-paragraphs-categorization huggingface dataset:
The second stage of data collection began on 17 Jun 2010 with a telephone campaign. At that time, there were 10,677 nonrespondent physicians, and a telephone number was known for 10,582 of them. The aim was to personally contact 7,000 of these physicians by phone to convince them to participate or, if they declared that they did not want to participate in the survey, to report their motives for refusal (for financial reasons, we could not contact all physicians). For this purpose, a sample of 9210 nonrespondents was drawn from among the 10,677 nonrespondents at the beginning of the telephone campaign (86. 26%). This sample comprised all physicians who had signed at least two death certificates (n=1565), and others who were included randomly. Each number was to be contacted up to 16 times before being abandoned (globally, the mean number of contact attempts was 4.
8). This number of call attempts was chosen based on efficiency and budgetary considerations. In parallel, a third letter, similar to the second one, was sent to 95 nonrespondent physicians whose phone number was unknown. This stage took place in two phases: 17 June-21 July, and 4 October-5 November. The third stage was a telephone survey of 1080 final nonrespondent physicians. It began on 7 December 2010 and ended on 7 January 2011. Each phone number was to be contacted up to 20 times before being abandoned. The goal of this survey was only to collect the motives for nonresponse, along with basic characteristics of the physicians (age group, sex, and specialty). To obtain an accurate measure of these, we set a high number of call attempts because we anticipated that these physicians would be very hard to contact (Figure  1  shows the data collection modes).
Statistical Analysis Samples were described using percentages and bivariate analyses with Pearson chi-square tests. Three multivariate logistic models (providing adjusted odds ratios and 95% confidence intervals) were also computed when comparing Web-based and paper questionnaires. These models tested whether the choice of Web-based questionnaires was linked to physician characteristics (Model 1), death characteristics (Model 2), or both sets of characteristics (Model 3). All statistics were computed using SAS V9. 3 and were nonweighted unless specified.
Preliminary Identification of Death Certificates, Physicians, and Participation Rate Overall, 14,080 death certificates (93. 87% of the initial sample of deaths) with identified physicians were available for the survey, corresponding to 11,828 different physicians (Table  1 ). The final sample was reduced to 13,460 deaths because of postal address problems (changes in professional location, etc). From this sample, 5217 questionnaires were completed and returned. This led to a participation rate of 40. 02%  [32]. We used the standard Response Rate 2 from the American Association for Public Opinion Research (AAPOR). The formula used was 5217 questionnaires/(5217 questionnaires + 1506 refusals + 449 letters not delivered + 561 physicians who could not respond because the survey did not concern them [sudden death, not the physician in charge of the patient, could not remember the case or could not find the file] + 49 other reasons for nonresponse [eg, death of respondent, retirement, not available during data collection] + E[6287 neither responding nor refusing]) = 40.
Survey of Nonrespondents The survey of the final nonrespondents, conducted by phone from December 2010 to January 2011 (the third stage of data collection), used a random sample of 1080 physicians with valid phone numbers who did not express explicit refusals but also did not respond during fieldwork. Contact was made in 957 cases, and 684 physicians could be contacted personally (Table  4 ). Finally, 547 physicians agreed to participate in the nonrespondent survey (79. 97% of all personally contacted physicians); 38 reported that they had already participated in the main survey and 52 said that they would still participate, leading to a response rate of 63. 74% (as above, this rate is computed following the AAPOR standard Response Rate 2). Among respondents, the most frequent motive for nonresponse to the main survey was lack of time (53.
Acknowledgments This paper is dedicated to the memory of  Martine Quaglia , who supervised the fieldwork. This survey could not have been completed without the financial support of  INED  and the  Health Ministry's Direction Générale de la Santé  (represented by  A. Fontaine  and  E. Gaillard ). We would like to thank the following individuals: The survey's steering committee for their support and constructive discussions throughout the design and first analysis of the survey ( Piernick Cressard  and  Francois Stefani  of the  Conseil National de l'Ordre des Médecins, National Medical Council ;  Eric Jougla ,  Albertine Aouba , and  Grégoire Rey  of  CepiDc at Institut National de la Santé et de la Recherche médicale -French National Institute of Health and Medical Research; Chantal Cases , Director of INED for her constant support throughout the survey; Our "trusted third party" partners:  Jeanne Fresson  of the  Department of Medical Information at the Maternité Universitaire de Nancy and Epiconcept  for the Internet response channel;  Régis Aubry (Physician, Head of the "Observatoire National de la Fin de vie")  and  Silvia Pontone (Physician) ; from the  Department.
Phase IV Patients with disease in question -randomised clinical trials -observational studies These studies and trials study the impact of applying the new intervention in clinical practice. This includes large randomised clinical trials, cluster randomised trials, and observational studies (clinical databases). For medical devices slightly different phases are described  104  Other fields like neurology, nephrology, endocrinology, hepatology, and surgery are less fortunate  5. Medical devices, nutrition, and rare diseases are considered fields especially in need of better clinical research  5, 6. The European Clinical Research Infrastructures Network (ECRIN)-Integrating Activity (IA) (http://www. ecrin. org/en/ cooperative-projects/ecrin-integrating-activityclinical-research-in-europe) has therefore identified barriers for good clinical research within these fields and how these barriers could be broken down in order to improve their evidencebased clinical practice  [7] [8] [9] [10].
ACKNOWLEDGEMENTS The  ECRIN-IA grant  from the  EU   FP7  provided support for meetings and for the conduct of this review. The  Mario Negri Institute  housed the ECRIN-IA meeting in February, 2013. The funding sources had no influence on data collection, design, analysis, interpretation; or any aspect pertinent to the study. The  ECRIN-IA grant  from the  EU   FP7  is thanked for support for meetings and for the conduct of this review. The  Mario Negri Institute  is thanked for housing the ECRIN-IA meeting in February, 2013. All participants of ECRIN-IA are thanked for participating in discussions identifying the bottlenecks of clinical research and the threats to internal validity of clinical research (and hence threats to external validity of clinical research) suggesting ways to blow up the bottlenecks and erase the threats.
Study design and sampling This study, part of the EU-funded SPOTLIGHT project  [26] , was conducted in five European urban regions: Ghent and suburbs (Belgium), Paris and inner suburbs (France), Budapest and suburbs (Hungary), the Randstad (a conurbation including Amsterdam, Rotterdam, the Hague and Utrecht in the Netherlands) and Greater London (United Kingdom). Sampling of neighborhoods and recruitment of participants have been described in detail elsewhere  [27]. Briefly, neighborhood sampling was based on a combination of residential density and socio-economic status (SES) data at the neighborhood level. This resulted in four pre-specified neighborhood types: low SES/low residential density, low SES/high residential density, high SES/low residential density and high SES/high residential density.
Multilevel regression analyses Because participants were nested within neighborhoods, the likelihood of being overweight for each partitioning variable was estimated by a multilevel logistic regression model (neighborhood identifier included as a random effect) adjusted for potential confounders (gender, age, education level, and neighborhood type). Statistical analyses were performed using R version 3. 2  [34]  ('R-part' package  [35] ), and STATA software (release 13. 0; Stata Corporation, College Station, TX, USA).
This study has some limitations, caution is thus needed when interpreting and generalizing the results. Due to its crosssectional nature, temporal relations between overweight and lifestyle behaviors cannot be assessed. As data were self-reported, potential (recall) bias, and possible underestimation or overestimation of variables (e. g. , weight/height  [63] , sedentary behaviors  [29, 64] , physical activity  [64] [65] [66] ) cannot be excluded. Although behaviors such as eating habits were not recorded in enough detail to assess the role of more detailed dietary aspects, such as macronutrient intake, many aspects of lifestyle currently thought to be associated with body weight were covered (sedentary behavior, physical activity, eating habits, alcohol consumption, smoking status, and sleep duration).
Acknowledgements The authors would like to thank all participants who took part in the SPOTLIGHT study. 1  Équipe de Recherche en Épidémiologie Nutritionnelle (EREN) ,  Université Paris 13 ,  Centre de Recherche en Épidémiologie et Statistiques, Inserm  ( U1153 ),  Inra  ( U1125 ),  Cnam ,  COMUE Sorbonne Paris Cité , Bobigny F-93017, France. 2  Université Paris-Est, Lab-Urba, Créteil, France. 3  Department of Epidemiology and Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands. 4  Department of Movement and Sport Sciences, Faculty of Medicine and Health Sciences, Ghent University, Ghent ,  Belgium. 5  ECOHOST -The Centre for Health and Social Change ,  London School of Hygiene and Tropical Medicine, London, UK. 6  Department of Preventive Medicine, Faculty of Public Health, University of Debrecen, Debrecen, Hungary.
178. 710-QS, Suprasil 1 , Hellma Analytics, Müllheim, Germany, V = 0. 080 mL) through the inlet tube of the spectrophotometer flow cell (V = 0. 137 mL) to measure drug concentrations continuously. Concentration values were recorded every minute over a total period of 150 minutes. All data was collected with UVProbe software ver. 2. 31 (Shimadzu Corporation, Kyoto, Japan). Initial drug concentrations in syringes were measured with the spectrophotometric UV_Vis method which was validated with six concentrations of between 25 and 400 μg/ mL for isosorbide dinitrate and seven concentrations of between 1 and 15 μg/mL for diazepam, repeated six times by the same person. The limits of detection (LOD) and quantification (LOQ) were respectively 2. 78 and 5. 57 μg/mL for isosorbide dinitrate and 0. 11 and 0.
ACKNOWLEGMENT The work is supported by the  European Research Council  through the  Actanthrope  project ( ERC Grant Agreement 340050 ), the  European  project  KOROIBOT  ( FP7 Grant Agreement 611909 ) and the  French National Research Agency  project  ENTRACTE  ( ANR Grant Agreement 13-CORD-002-01 ). We warmly thank  Bruno Watier  from  LAAS-CNRS and University of Toulouse  for helping us in the acquisition of motion capture data.
Analyses The model population was analyzed using SAS software (SAS Institute, Cary, North Carolina) and results are presented for 35 540 individuals (model sampling rate 1/1000). One-way sensitivity analyses to assess uncertainty around the results were performed on the preceding (2012-13) and following (2014-15) influenza seasons, which had slightly lower and higher RTI rates, respectively, and across the lower and upper 95% confidence limits for reduced duration of RTI and reduced incidence of RTI.
e. Can$7-17 per week. This suggests that the acquisition cost of probiotics is largely inferior to the WTP for healthy food, and this without any incentives (aside private advertising). Other out-of-pockets expenses that were not included in this model due to lack of data were over-the-counter (OTC) medication and costs related to informal care for a sick parent or child. The above mentioned costs would be part of a so-called 'society' perspective, along with any Government expenses on campaigns or advertisements to, for example, encourage healthy lifestyle choices in the population. However, this fell beyond the scope of our analysis. Importantly, the current incidence data were representative of individuals consulting a GP for their RTI. They represent only a very small proportion of RTI sufferers, and therefore, the real savings may be higher than reported.
And lastly, because Statistics Canada does not report the all-cause consultation rate on the population < 12 year old, we assumed this age group was equal to the ! 65 year age group in terms of visit rate, as was observed in the US statistics  [62]. These modifications can artificially inflate or deflate the RTI incidence at various points during the year. These limitations may have consequences on the model outcomes when examined by subgroup, as we assumed that risk factor prevalence and effects were independent. The overall impact on the Canadian data is not considered to be high, as the model remained correctly anchored on national statistics, for 3 years in a row. In terms of economic results, there is some uncertainty around the duration of ILI symptoms potentially requiring medications; package size of analgesics/antipyretics was estimated based on the duration of RTI episodes in the placebo groups of the meta-analyses (7-8 days) while the cost of an antibiotic course was directly provided by a Canadian health information website.
Acknowledgments We thank  Statistics Canada  and the  Flu Watch team at the Public Health Agency of Canada  for providing the core demographic and epidemiological data used in this model. We thank  Stephen Freedman  and  Sisira Sarma  for their scientific guidance on model assumptions specific to Canada,  Donna Coffin  for her contribution in local and burden-of-illness data collection, and  Elyonore Tsakeu  for supporting the statistical analyses.
Methods The analysis comprised a simulation  [13] [14] [15]  based on results from combined previous observational studies, randomized clinical trials, and a meta-analysis with the following steps: 1) estimation of a) the survival function of HF-HD patients from the Membrane Permeability Outcome Study  16  dataset and b) estimation of the survival function of HDF patients using the risk reduction estimates due to the treatment effect from the meta-analysis of Mostovaya et al  17  (even though it includes also studies comparing HDF to low-flux HD, it was considered the best proxy with respect to other alternative meta-analyses available in the literature); 2) simulation of the survival of the same sample of patients as if allocated to HF-HD or HDF using three-state Markov models; 3) application of state-specific HRQoL coefficients and differential costs (materials, testing, and consumption of water) derived from the literature.
Background As the numbers of patients co-infected with human immunodeficiency virus (HIV) and tuberculosis (TB) increase in sub-Saharan Africa, reliance on paper-based registers may result in poor record management and, ultimately, suboptimal clinical care. One innovative approach to improve data quality and strengthen the continuum of care is the implementation of user-friendly, real-time electronic medical record (EMR) system. EMR systems may be well received by clinicians  [1] , improve the quality of clinic data  [2] , and increase the quality of patient care by ensuring adherence to clinical guidelines and reducing errors  [3, 4]. In 2005, Lighthouse Trust, the largest public provider of antiretroviral therapy (ART) and TB services in Malawi's central region, in partnership with Baobab Health Trust, a local non-governmental organisation that leads the development of EMR system in the country, developed and launched an ART-focused EMR system.
By 2006, both of Lighthouse Trust's clinics, Lighthouse and Martin Preuss centre (MPC), successfully employed the real-time, touchscreen-based EMR system to manage ART patients. Data management for more than 36,000 ART patients at both clinics is now entirely based on the EMR system resulting in near completeness of patient level data. Due to the EMR system success in improving both patient management and reliable record keeping, described previously  [5] , the Ministry of Health (MoH) formally adopted and implemented Baobab's EMR system in high burden ART sites, scaling up to 39 ART facilities with more than 2500 patients by 2013  [6]. At national level, monitoring and evaluation of the ART program, including timely generation of ART supervision reports, improved dramatically as a result of the ART EMR system roll-out.
Although the current ART EMR system in Malawi is successful, one significant limitation is its singular programmatic focus. Nationally, almost 60 % of TB patients are co-infected with HIV  [7]. At MPC clinic, Lighthouse's integrated TB/HIV clinic run in partnership with the District Health Office (DHO)  [8] , almost 50 % of its 3500 TB suspects and 60 % of 3200 TB patients are TB/ HIV co-infected. These co-morbidities resulted in information for TB/HIV patients being stored in dual record systems: TB-related information for TB suspects and TB patients was recorded in paper registers while ART data for these same patients were recorded in the ART EMR system. Subsequently, paper data for all TB and TB/ HIV co-infected patients were entered retrospectively in an off-line Microsoft Access database; data for those coinfected was also linked to ART EMR system information using unique patient identifiers.
This dual-record system created immense challenges as paper-based TB registers were often incomplete with some treatment and followup information missing, thus creating barriers in TB/HIV service integration, reducing program quality, and diminishing data quality for program monitoring. In 2010, Lighthouse Trust, with its partners Baobab Health Trust, the Malawi National TB Program (NTP), and the IeDEA Collaboration at the Institute of Social and Preventive Medicine at University of Bern in Switzerland, received a one-year innovation development grant from US President's Emergency Plan for AIDS Relief (PEPFAR) to develop and implement a real-time, touchscreen-based EMR system for TB and TB/HIV integrated care. The TB/HIV EMR system is expected to significantly increase service quality and facilitate program monitoring at MPC.
General system description As with the ART EMR, the TB/HIV EMR system is a realtime, point-of-care, intranet-based system based on open source software that employs the MySQL OpenMRS data model  [10]. Key TB/HIV EMR system components include: registration of demographics, anthropometric measurements, management of laboratory samples and results (Smear microscopy of sputum, CD4 count, viral load etc. ), HIV testing, TB diagnosis, WHO HIV clinical staging, family planning services, TB/HIV clinical review and drug dispensing. Other co-morbidities and infections are also documented during clinical review including diabetes, cancer, meningitis, and malaria. The TB/HIV EMR system uses touchscreen technology with terminals in reception and clinical consultation rooms. All TB-and HIV-related information is available through the same touchscreen at the terminal.
Each terminal is equipped with a hand-held barcode scanner and a small printer, issuing unique patient id, filing number and duplicative visit summary stickers (labels) for patient files and health passports (Fig. 1a, b ). Through the intranet, each terminal is connected to a central server where data from ART EMR and TB/HIV EMR modules are stored in the same database. The data are stored and backed-up nightly, onsite and off-site. The terminals do not provide access to internet or other software applications. Figure  2  shows the dashboard (first page) of the system.
Pre-testing system components with key clinic users As components of the system were being developed, a series of provider-focused pre-testing sessions were organized with mixed groups of up to 10, total, clinicians, clerks, and data staff to assess clarity and order of questions, and to verify compliance to national guidelines. We also tested various patient flow scenarios to ensure skip patterns were followed, e. g. family planning questions were not asked to young children or assessment of ART eligibility in confirmed HIV-negative patients. Also, pre-test sessions ensured that system alerts designed to remind clinicians to conduct HIV tests, order follow-up lab tests, consider drug contraindications, etc. , worked as expected. Early pre-testing of TB/HIV EMR question flow and content helped system users understand the questions, identify missing or misplaced content, and avoid errors in misinterpretation.
Security concerns EMR systems require strict data security protocols to ensure both patient confidentiality and data privacy. The TB/HIV EMR system has several built-in security features: (1) each user (service provider, system developer, IT support team etc. ) has a unique username and password for logging in; (2) each user or cadre is restricted to their specific domain (e. g. service area); (3) only select "super users" may edit or revise patient entries; (4) patient information accessed by providers is restricted to the current patient (through entering patient's unique ID or scanning its barcode); (5) data are stored in a passwordprotected, database and are accessible only by highlytrained internal staff; (6) the TB/HIV EMR system keeps an audit trail by tracking users, locations and timestamps for all data changes; and (7) data is encrypted during offsite data back up using secure encryption protocols.
Acknowledgements The authors would like to thank all the staff who collected data at Lighthouse and  Martin Preuss center clinics. Hannock Tweya  is supported as a  Senior Operational Research Fellow  by the  International Union Against Tuberculosis and Lung Disease, Paris, France. Caryl Feldacker  was supported with funding from  Cooperative Agreement   U91HA06801  from the  US Department of Health and Human Services, Health Resources and Services Administration (HRSA). Olivia Keiser  is supported by a  PROSPER grant  from the  Swiss National Science Foundation.
Conclusions: The PMSI-MCO database is a major source of data on the health status of the population. It can be used for the area-by-area observation of the performance of certain healthcare indicators, such as in-hospital mortality, or to follow the implementation of the National Stroke Plan. Our study showed the interplay between social and demographic factors and stroke-related in-hospital mortality. The map derived from the results of the exploratory analysis illustrated a variety of areas where social difficulties, aging and high mortality seemed to meet. The study raises questions about access to neuro-vascular care in isolated areas and in those in demographic decline. Telemedicine appears to be the solution favored by decision makers. The aging of the population managed for stroke must not mask the growing incidence in younger people, which raises questions about the development of classical (smoking, hypertension) or new (drug abuse) risk factors.