dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS AND DISCUSSION The main characteristics of the collected data are given in Table  1. Among the workers, 12 are still This database clearly shows that changes in the cardiovascular system (e. g. systolic and diastolic pressure) of the workers had been carefully monitored. The number of medical examinations and blood tests is sufficient to provide reliable information about a relevant parameter for further epidemiological or aetiological research. Table  3  shows the completeness and the availability of specific variables of interest. It ranged between 1. 2 % for HDL cholesterol and 99. 8 % for granulocytes,.
One major advantage of our approach is that for most of the parameters it can provide not only one value per individual, but the whole variation over time. For example, every worker had a mean of 23 systolic blood pressure measurements over the median followup of 20 years. The Framingham Cardiac Risk Score (  22 ) is a well-known tool to predict 10-year cardiovascular risks for an individual. It requires seven variables to calculate the risk: sex, age, total cholesterol, HDL cholesterol, systolic and diastolic blood pressure, diabetic status, and smoking status. The availability of data for all the required parameters in our dataset is good, with the notable exception of HDL cholesterol (which is available for only 33 workers). The diabetic status can be determined on the basis of blood sugar levels.
Potential use in the upcoming nested case-control study The collected data will be used in the upcoming nested case-control study as co-variables in addition to the main exposure variables (e. g. exposure to ionising radiation and uranium compounds) to investigate how they could have affected the results of the previous cohort study by Guseva Canu et al. (2). In that study, the effects of exposure to uranium compounds on cardiovascular mortality were assessed without taking into account the known cardiovascular risk factors, as they were not available at the time. Therefore, the collected variables will primarily be analysed as possible confounding or modifying factors using a conditional logistic model. These analyses should be able to handle time-dependent covariates because of the longitudinal changes in blood tests and anthropometry over time.
Potential for broader use The large set of collected haematological data will allow us to address the hypotheses on the association between haematological parameters/hypertension and internal uranium contamination. For instance, Wagner et al. (25)  found that exposed women ran a higher risk of elevated systolic pressure. The use of longitudinal data makes it possible to evaluate correlations within and between workers using generalised estimating equations  (26). Thus, in addition to assessing the modifying or confounding effects on cardiovascular mortality, OM data can also be used independently, as an intermediate health outcome to assess the effect of uranium exposure on health parameters such as blood pressure. In that sense, OM data could also be used for aetiological research. Kathren et al.
A major limitation to the use of OM data for epidemiological research are the missing or incorrect values in the database. However, this issue can be addressed with frequentist  (31)  or Bayesian  (32)  or other methods  (33). If there is no pattern to missing data, this could be resolved by careful data management, but if the errors are systematic, Bayesian method could help to fill them in. As biobanks are very difficult to set up in France for ethical and economic reasons, the electronic storage of half a century of archived OM data on nuclear workers is a cost-effective alternative. Such data are generally directly available, accurate, and free of recall bias. Most are systematic (annual) biological measurements that span the worker's career and follow strict medical surveillance procedures.
Acknowledgements The authors wish to thank  Olivier Laurent  from the  Laboratoire d'épidémiologie  for carefully reading the drafts,  Iris Jovanovic  for her major role in creating the computer database, the personnel of the  Pierrelatte Occupational Medicine Department  for great help in logistics, and  Dr Alain Acker  ( AREVA ) for his help in accessing the workers' medical records in the Pierrelatte OM department.
Methods We conducted a multicenter, prospective cohort study of cancer outpatients from the beginning of treatment and until three months after the end of treatment. The protocol was approved by the regional ethics committee of the University Hospital of Besançon for both participating hospitals (Comité de Protection des Personnes Est-II, France), the National French Data Protection Agency, and was supported by a regional grant from the French National Hospital Research Programme (Programme Hospitalier de Recherche Clinique, PHRC). All patients provided written informed consent.
The significance level for multivariate analysis was set at α = 0. 005 (according to Bonferroni correction for multiple testing). All tests were two-sided. For the interpretation of the satisfaction scores, we considered the minimal difference defined as clinically meaningful by Osoba et al. , namely a mean change of at least 5 points  [26]. Statistical analysis was performed using Statistical Analysis Software (version 9. 1, SAS Institute, Cary, NC, USA).
Missing data on the OUT-PATSAT35 questionnaires Missing data in questionnaires were identified as MNAR, so each scale score was estimated with a multiple imputation by age and treatment type (radiotherapy vs. chemotherapy). Six patients were non-responders at baseline and were excluded from the analysis. 49 patients responded at baseline only, and 79 patients responded at the beginning and at the end of treatment but not at 3 months after treatment. Comparison of characteristics between these 128 patients lost to follow-up (19% of the all those included) and the 561 patients who answered at all 3 assessment time points revealed a lower level of QoL at baseline and a higher frequency of lung or head and neck cancers.
Acknowledgements The authors thank  Fiona Ecarnot  for editorial assistance. This publication was supported by the  French National Hospital Research Programme  ( Programme Hospitalier de Recherche Clinique , PHRC 2004) Part of this work has been presented as a poster presentation at the 37th ESMO congress (28 Sept-2 Oct 2012 in Vienna) and the 19 th ISOQOL congress (24-27 Oct 2012 in Budapest).
Acknowledgements We acknowledge  Dr. Gabriella De Carli  for scientific advice and English editing and  Ms. Ramona Iacovino  for her administrative support during the project. This work was supported by the  EC grant EuroNHID  ( 2006205 ) and by the  Ministero della Salute ,  Italia-Ricerca Corrente ,  Istituti di Ricovero e Cura a Carattere Scientifico.
Information gathering Mortality data: Information was obtained for total daily deaths registered in Bogota, DC based on death certificates. Information was used of those who at the moment of their death were residents of DC. The outcomes analyzed were all causes of death (ICD: A00-T98), respiratory causes (ICD: J00-J98) and cardiovascular causes (ICD: I00-I99), according to the International Classification of Diseases (ICD), version 10. Meteorological and air pollution data: Hourly data on atmospheric pollutants were obtained from the Bogota Air Quality Monitoring Network (RMCAB, abbreviation in Spanish), which has 14 stations distributed across the entire city (figure  1 ) and monitor PM 10 using the Beta-Attenuation Method (BAM). Temperature and relative humidity (RH) registries were obtained from the RMCAB as well as Institute of Hydrology, Meteorology and Environmental Studies (IDEAM, abbreviation in Spanish) stations.
Evaluation of exposure: Daily (24-hr) averages of PM 10 were calculated for all the monitoring stations. The criteria for sufficiency was 75% of data, which indicates that of the 24 registries obtained each day, there must be at least 18 valid data sets in order to calculate the average for a particular day. Later, to assign exposure, daily average PM 10 and 8-hr averages for O 3 (between 10am and 6pm) were calculated city-wide. With regard to temperature and RH information, the daily (24 hr) average was calculated for each parameter, also with 75% of data as the criteria for sufficiency. Statistical analysis: Since the analysis encompasses nine years, time trends and seasonality were controlled using natural spline functions (ns), with 1 to 5 degrees of freedom (df) per year. This number varied according to whether the models showed a better fit to the data.
[23] [24] [25] [26]  A maximum lag of five days before the deaths was chosen for the exposure variable using DLM. 24, 26 The statistical analysis was performed using Stata software version 9. 0 and R version 2. 8. 1 (R Project for Statistical Computing, http://www. r-project. org), using the ares library for the ESCALA Project (Multi-city Study of Air Pollution and Health effects in Latin America). 27.
Acknowledgments The authors would like to thank  IDEAM , the District Secretary for the Environment and the  District Secretary of Health  for supplying the information to conduct this work, and  Irma Soyachi Salgado  for her administrative work. The principal author dedicates this work to the memory of  Orlando Blanco Casteñeda (R. I. P. ), loving, exemplary and dedicated father, whose teachings laid the ethical, educational and personal foundations of this investigator. Thank you Dad.
M entorship has been defined as 'providing an en- abling relationship that facilitates another's personal growth and development'. 1 Strong academic mentorship is a key feature of the operational research (OR) courses run by the International Union Against Tuberculosis and Lung Disease (The Union) and Médecins Sans Frontières (MSF). 2, 3 The OR courses consist of three separate, interlinked modules each of 5 or 6 days' duration: Module 1 focuses on developing a research protocol, including ethics considerations; Module 2 focuses on electronic quality-assured data collection and analysis using open access software (EpiData, Odense, Denmark); and Module 3 focuses on preparing a paper for submission (with the participant as first author) to a peer-reviewed journal and linking research to policy and practice.
The teaching format for all three modules includes lectures, small group break-out sessions with mentors, and plenary sessions where participants present their work to the group and receive feedback from mentors and peers. Between Modules 1 and 3, specific milestones (submission of the protocol, completed ethics forms and EpiData files and submission of proof of data collection to course coordinators) must be achieved within pre-determined time frames for participants to proceed to Module 3. A scientific paper must then be submitted to a peer-reviewed scientific journal within 1 month of completing Module 3 for the participant to fulfil the requirements of the course and be entitled to receive the course certificate. Most courses have 12 participants. In Modules 1 and 3, there are usually eight mentors working in four pairs, with each pair mentoring three participants.
Background: In the age of big data in healthcare, automated comparison of medical diagnoses in large scale databases is a key issue. Our objectives were: 1) to formally define and identify cases of independence between last hospitalization main diagnosis (MD) and death registry underlying cause of death (UCD) for deceased subjects hospitalized in their last year of life; 2) to study their distribution according to socio-demographic and medico-administrative variables; 3) to discuss the interest of this method in the specific context of hospital quality of care assessment. Methods: 1) Elaboration of an algorithm comparing MD and UCD, relying on Iris, a coding system based on international standards. 2) Application to 421,460 beneficiaries of the general health insurance regime (which covers 70% of French population) hospitalized and deceased in 2008-2009.
Background Because of their richness, availability and marginal cost, medico-administrative data are increasingly used for epidemiological studies and health care performance assessment  [1]. The linkage of different databases gives even more possibilities to address important public health questions. However, the mere juxtaposition of information may be insufficient and the data sometimes need to be studied in relation with each other. In particular, the relationship between medical diagnoses recorded at different times or in different contexts for an individual may be of interest. For example, the study of the frequency and causes of death after medical care may be very informative, whether focalized on a specific care and specific subsequent mortality causes  [2] , or in more general approaches like quality of hospital care assessment.
Along this line, there is a growing interest in assessing the contribution of the causes of death information to the building of post-hospital mortality indicators  [3]. However, comparing two medical diagnoses in order to assess whether they refer to a unique morbid process (or in other words whether they are consistent), or not, is complex. Moreover, given the huge size of national databases, this complex comparison needs to be performed automatically. Hospital diagnoses have been compared to the underlying causes of death in a few studies on death certification quality assessment  [4] [5] [6] [7] [8]. In this context, Johansson and Westerling have proposed in 2002 a method of comparison of ICD9 codes  [7]. This method takes advantage of the validated automatic systems developed for the selection of the underlying cause of death.
These systems, following very precise international definitions and procedures, are designed to check the chain of events leading to death by testing causal relations between medical conditions. Iris  [9, 10] , a validated piece of software which relies on the current international standards of death certification  [11] , makes the update and an extension of this method possible. Our purpose was to test the feasibility of using such a system in order to compare individual diagnoses derived from the French hospital and causes of death databases. The aim of this study was 1) to propose and test a reproducible, automatic method of comparison of the main diagnosis of last hospital stay to the underlying cause of death in order to determine their consistency or independence 2) to study the distribution of consistency and independence according to socio-demographic and medico-administrative variables for deceased subjects hospitalized at least once during their last year of life.
Hospitalization data The French acute care hospital database (PMSI-MCO)  [13]  is designed for hospital payment. It provides medical information for all patients discharged from short-stay hospitals, both public and private. Patient's stays are chained so that the number of hospitalizations within the year before death can be calculated. This study focuses on the last hospitalization before death (both occurring in 2008 or 2009). The patient's gender, age (at admission), and the main diagnosis (ICD10 code) were extracted. The hospitalization database is included in the Social Security database (SNIIRAM)  [14].
Besides, since the discharge-death time interval was imprecise for 2008, the 2008 records were considered only when death occurred in hospital, or 3 months or more after discharge (exact day of discharge available in 2009, month of discharge only in 2008). The final database comprises 421,460 subjects deceased in the year following their last discharge. The linkage of the hospitalization and cause of death data and the study of the resulting dataset were approved by the two French data protection committee and institutional ethical review boards concerned: Institut des Données de Santé (authorization n°16-24/11/ 2010) and Commission Nationale de l'Informatique et des Libertés (authorization n°1454315).
Definitions The French definition of the main diagnosis has changed during the study period, from "condition that takes up the majority of resource use during the hospital stay" before march 2009, to "final diagnosis explaining hospital admission" after. However, this modification of definition had no impact on our results (results not shown). In order to capture the pathology, which is the relevant information in our purpose, when the main diagnosis of the hospital database was a chapter XXI code (Factors influencing health status and contact with health services), the "main diagnosis" (MD) mentioned in this article was defined as the related diagnosis. UCD is defined in volume 2 of ICD-10th revision as "(a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury".
Algorithm In order to classify each death in one of these four cases, an algorithm was designed to compare MD of last stay and UCD, taking all medical conditions mentioned on the death certificate into account (see Additional file 1). At four stages of the algorithm, the type of relationship between MD and UCD was given by running Iris software (V. 4. 0. 38) on "test certificates" (see Additional file 2). Iris is a language-independent coding system using international standards  [9, 10]  (see Additional file 3): the WHO ICD-10 classification, rules and guidelines as well as the knowledge base of the Mortality Medical Data System (MMDS)  [15, 16] , ACME (Automatic Classification of Medical Entry) software in particular  [17, 18]. Artificially introducing MD in a test certificate, at a specific place according to the question asked, permitted us to assess its potential participation in the causal sequence leading to death  (5).
Conclusions The method presented in this paper permits us to obtain more structured and exploitable information from large hospital and mortality datasets. It is still to be improved, but the distribution of the relation obtained is mainly compatible with what would be expected. Causes of death could improve hospital mortality indicators built for evaluating and improving hospital quality and future research on post-hospital mortality indicators should take the notion of independence between hospital diagnoses and underlying cause of death into account. More generally, this method has the potential of being developed and used for other diagnoses comparisons across time periods or databases.
Methods This study presents results of an uninterrupted time series study design that started in 1949. The same protocol has been followed throughout. Here we reviewed annual data on electrical shocks for the 1996-2005 study period. To obtain an exhaustive collection of each electrical incident with sick leave, we reviewed databases from several EDF sources: the Prevention and Safety Department provided statistical data and technical assessments; the Medical Department of Specific Health Insurance fund, provided anonymous electrical incident medical forms; and the Medical Compensation Committee which provided data on the workers' compensation, return to work and other occupational aspects. The information for each injury was recorded on an anonymous and a confidential routine sheet with 52 items to be completed by the occupational physician.
These items are global data (age, voltage, age, gender, the type of work, day of the week); burns characteristics (mechanism, degrees of the burn, location, burn standard units); burn sequelae; functional burn sequelae; unaesthetic sequelae (scarring) associated with burns; cardio-respiratory sequelae; Neuropsychiatric sequelae; neurological sequelae; sensory sequelae; sick leave and disability rates. The data sets were analysed statistically with SAS V9. 2 software. Confidence intervals (95%) were calculated with Poisson regression for rate data to evaluate the trends in electrical trauma consequences. We are used Fisher's exact test to examine the differences in severity of electrical injuries and psychological sequelae among groups exposed to different voltage levels.
Acknowledgements The authors thank EDF's experts who contributed to the study by completing the anonymous electrical incident medical forms and providing medical clarification, if required. We would also like to thank  CEMKA Eval  for carrying out the statistical analyses, and  Monique Bonnet-Belfais  and  Veronique Ezratty  who gave their time and expertise. This manuscript is dedicated to  Dr Elisabeth Gourbie  `re and her involvement in prevention and research on electrical burns.
Unfortunately, these measures were partly offset by the expansion of malaria. This is apparent in the mortality data routinely collected in Antananarivo that we describe below. In February 1906, during the rainy season, 57% of deaths were due to malaria, against only 10% in February 1903; it went back down to 35% in February 1907. This expansion of malaria was linked to a broad set of factors, including large movements of population induced by infrastructure projects, changes in the methods used for rice cultivation (rice plants were no longer dried out after the harvest), and the settlement of some populations closer to the rice fields  (19). The French administration established quinine depots, but malaria nonetheless became endemic in the central highlands. The colonial administration also developed the city by clearing some marshland, setting up a public lighting system in 1910 and installing the first standpipes in 1911  (20).
Based on data for the period 1984Á1995, Waltisperger et al. showed that agespecific mortality rates based on the BMH conformed to standard age patterns of mortality, and concluded that the data presented no indication of underreporting of deaths  (34). For this analysis, we retained only the deaths of residents of Antananarivo. Causes of death were recorded according to the Ninth Revision of International Statistical Classification of Diseases (ICD-9). The same physician was in charge of recoding all deaths that had not been previously coded in hospitals or clinics. Causes were later consolidated into seven broad categories: 1) infectious diseases (classified in the first chapter of the ICD-9), 2) neoplasms, 3) nutritional deficiencies, 4) cardiovascular diseases (CVDs), 5) diseases of the respiratory system (including influenza and bronchitis), 6) injuries and accidents, and 7) other diseases.
Changes in the rankings of the leading causes of deaths (1900Á2012) To investigate changes in the cause-of-death structure, we distinguish three periods: 1900Á1903 (the first uninterrupted series of monthly mortality reports by cause), 1976Á1980 (the first 5 years covered by the database of individual death records), and 2008Á2012 (the most recent years). Because the classifications of diseases changed between 1900 and 1976, as we discuss below, we will only examine the rankings of broad categories of causes of deaths. Problems remain, however, because some categories of causes such as nutritional deficiencies (corresponding to ICD-9 codes 260Á269) were not singled out in 1900Á1903. Measles was not mentioned among causes of deaths either, whereas it accounted for about 4% of deaths in 1976Á1980.
Discussion Death registers of Antananarivo represent a valuable population-based mortality database for Madagascar, but they nonetheless suffer from important shortcomings. First, although deemed very high, the completeness of death reporting in the capital remains unknown and it certainly changed over time. For the period prior to 1960, there is no estimate of the completeness; we can only count on the fact that the health situation was more closely monitored in the capital than anywhere else because it was the seat of the General Government and had a large European population  (16). After 1960, there are some signs that most deaths were captured in the vital registration system. In 1965, the CDR for the larger area of the province in which the city is located (10Á11) was very close to the estimate based on the 1966 demographic survey (13Á19).