dataesr/scientific-paragraphs-categorization huggingface dataset:
Another limitation is that the present study did not assess the long-term outcomes of the various bariatric procedures and the consequences of choosing 1 technique rather than another. Lastly, our database is unable to distinguish between RYGB and omega loop gastric bypass due to the lack of a specific code for this procedure in the database. The growth of bariatric surgery as a major treatment option for severe obesity in many countries raises a number of challenges. At first, the large and increasing number of procedures constitutes a challenge for the health system to provide the recommended postoperative follow-up. While >450,000 patients will have undergone bariatric surgery in France in 2017, organization of medical care of this growing number of patients, including wellthought access and choices of procedure, but also good quality long-term follow-up for all, constitutes a major challenge.
Besides 1000 Hz, the following frequencies were tested in random order: 250, 500, 1000, 2000, 3000, and 4000 Hz. The hearing thresholds and MCLs were measured separately in the left and right ears. The hearingthreshold and MCL measurements were followed by a series of two-alternative forced-choice (2AFC) interaural-comparison tests. The main test was an interaural pitch-comparison test. This test was preceded by an interaural loudness-comparison test. The goal of the preliminary loudness-comparison test was to collect data that would be used to individually adjust the stimulus level in the left and right ears so as to reduce loudness differences between the two ears; this was done to limit any influence of interaural loudness differences on pitch comparisons across the two ears. In both tests, the participant heard two consecutive pure-tones (each 500-ms in duration including 50-ms on and off linear ramps, separated by a 200-ms silent gap) on each trial.
Four participants had hearing thresholds larger than 75 dB HL at Fc; of these four participants, three had statistically significant binaural diplacusis at Fc. It is possible that, for these listeners, the binaural diplacusis was related to cochlear dead regions. However, considering that the majority of participants in the study showed significant binaural diplacusis even though their hearing threshold were all lower than 75 dB HL, cochlear dead regions do not appear to be a necessary condition for binaural diplacusis. While the results of the present study are, for the most part, consistent with an injuryinduced basal-shift in the cochlear mechanics, they support the view that pitch perception depends on a place code  [1] [2]. However, based on different datasets, other authors have reached a different conclusion.
Objective: The present paper investigates on lay people's beliefs regarding cancer risk factors' and their correlates, especially people's socioeconomic status (SES), as they may heavily contribute to social health inequalities. Methods: We used data from the 2010 Baromètre Cancer, a national representative telephone survey conducted in France (N = 3359, age 15-75, participation rate 52%). Results: Respondents differentiate behavioral factors (smoking, drinking, unprotected sun exposure, etc. ), environmental risk factors (air pollution, chemicals in food, etc. ) and psychosocial risk factors (stress, painful experiences, etc. ) for cancer. Those with a higher SES were more likely to emphasize behavioral and psychosocial factors, while those with an intermediate SES were more likely to do so for environmental ones.
Sampling design and data collection We used data from the second Cancer KABP survey, a survey on cancer-related knowledge, attitudes, beliefs and practices conducted by the National Institute for Prevention and Health Education (INPES). This telephone survey (using a computer-assisted telephone interview system) was carried out in 2010 on a representative random sample of the general population aged 15-85, based on a two-stage random sampling design (first selecting households by phone number, secondly selecting an individual within each participating household). People not speaking French and residents of retirement homes, hospitals, and other institutions were excluded from the survey. The participation rate was 52%. Questions related to perceptions of cancer risk factors were only asked to individuals without a personal history of cancer and aged 15-75 (n = 3359).
The questionnaire also investigated respondents' general opinion on cancer, with an item specifically designed to identify fatalistic attitudes ("Nothing can be done to avoid cancer": 'strongly disagree', 'somewhat disagree', 'somewhat agree', 'strongly agree', 'don't know'), as well as respondents' smoking status (with a binary indicator spotting current daily smokers, i. e. respondents who reported that they smoked at least one cigarette per day at the time of the survey). Other data collected included respondents' main information sources on cancer (they had to choose two sources among the following ones: TV/radio/newspapers, Internet, relatives, health professionals, health magazines), and having at least one relative with a history of cancer (yes/no). Indeed another important aspect regarding lay people' perceptions of cancer risk factors is the kind of information they use, especially in the Internet society  (Befort et al.
Statistical analysis Data were weighted to match the sample more closely to the French population for age, gender, educational level, geographic area and size of residency town. All analyses were performed with weighted data. We first conducted a principal component analysis (PCA) on the 14 items related to perceptions of risk factors (with the following coding: certainly not = 1, probably not = 2, probably = 3, certainly = 4, don't know/missing value = 2. 5). PCA is a useful statistical method for identifying correlational patterns in a large data set as it highlights the strongest bivariate correlations existing between selected variables. It is a useful preliminary step before combining numerical variables into a synthetic score. As we assumed that people's perceptions were shaped by their health locus of control and by their beliefs toward the psychogenesis of cancer, we expected strong positive correlations between perceptions related to risk factors pertaining to the same type (either behavioral, environmental or psychosocial) (Hypothesis 1).
Conclusion Lay people's perceptions regarding cancer risk factors are embedded in social and cultural contexts: they reflect people's experience and beliefs (especially regarding one's body and health). As these perceptions are significant correlates of cancer-related attitudes and behaviors, their SES disparities may contribute to social health inequalities. But, in order to tackle them properly, prevention policies should not consider they simply reflect ignorance or misinformation. Supplementary data to this article can be found online at http://dx. doi. org/10. 1016/j. pmedr. 2016. 01. 008.
Data collection at food assistance centers In both surveys, data collection was carried out between November and April, the period during which food assistance is highest in France. Trained dieticians collected information on standardized questionnaires that included socio-demographic and economic characteristics, dietary behavior and food supply, use of food assistance, food insecurity and health characteristics. If possible, interviews were carried out in separate rooms for confidentiality and accuracy. In 2004-2005 and 2011-2012, socio-demographic and economic data included age, gender, marital status, number of children, place of birth, housing type, education, employment and household income, including social assistance. Dietary behavior included frequency of usual food consumption on a daily basis ("bread, toast, breakfast cereal"; "rice, pasta, potatoes, semolina"; "vegetables (except for potatoes)"; "fruits, including 100 % fruit juice"; "dairy"; "meat, poultry, eggs") with eight categories, from "never" to "4 times a day or more".
Acknowledgements The authors are grateful to the dieticians who collected the data; to the food assistance structures that participated in the survey; to the health centers of the health insurance system and the municipalities in which health examinations were carried out. We thank  Valérie Féron  and  Alexandra Delannoy  ( Observatoire de Santé Ile-de-France ), who contributed to statistical analyses, and  Jerri Bram , who edited the manuscript.
These differences, however, were no longer statistically significant once other factors had been controlled for. The view that the class distribution of job insecurity has changed over time was confirmed when a comparison was made with Skills and Employment Survey series data for 1986 (not shown). In the earlier period, both semi-skilled and especially routine employees had significantly higher levels of job tenure insecurity than all higher classes. Class did still matter for job insecurity in 2012, but this was in relation to job status insecurity rather than job tenure insecurity. Even with other factors controlled, those in lower class positions had significantly higher job status insecurity than those in managerial or professional occupations. This is likely to reflect the persistence of traditional hierarchical relations, in which the costs of internal flexibility fell primarily on those in the lower ranks.
For instance, given the predominance of mountainous land in Fukushima, the role of the forest as a potential secondary source of contamination dissemination to other parts of the environment by runoff and erosion processes was examined  13. In this context, monitoring contaminated forest stands is essential to providing local datasets that will allow flows between forest ecosystem compartments and driving parameters to be quantified  14, 15. Many studies in the Fukushima contaminated area related to early 137 Cs distribution in the Japanese forest ecosystems  2, [15] [16] [17] [18] [19]. Most of these studies began immediately after the accident (main data from 2011 to 2012) and showed that for the evergreen species, interception by canopies was very high (60-90%) due to high canopy closure  20.
1 ). The distributions of the stem compartment (outer bark, inner bark, sapwood and heartwood) biomasses were calculated using the successive truncated cone model (circular section), calibrated by specific biometrics on wood discs. These distributions were validated by comparing aggregated modelled biomasses of stem organs with measured stem biomasses (mean difference of 5%, maximum difference of 12%) (Fig. 1 ). The biomasses of the root compartments were calculated using the allometric equations reported by Lim et al. 27  for total biomass (W = a + bD 2 , where W is the dry biomass in g, D is the DBH in cm, a = 1,198. 17 and b = 85. 044), and the distribution between fine (< 2 mm) and coarse (> 2 mm) roots was derived from Fujimaki et al. 26  data according to the age of the stand. With regard to the dead organs (branches and needles) of the MC stand, the allometric equations published by Yoshida and Hijii  28  were used (W = a (D 2 ) b , where W is the dry biomass in g, D is the DBH in cm, a = 95.
7 and b = 0. 728 for dead needles, a = 16. 1 and b = 0. 970 for dead branches). The parameters of these allometric equations were obtained from a specific stand with dendrometric characteristics that are very similar to those of the MC stand. For the dead organs (branches + needles: dead material in-crown) of the YC stand, exhibiting different dendrometric characteristics, we used a regression model, plotting the log-transformed literature values reported for younger stands in the literature  [28] [29] [30]  against their log-transformed height (H) values (N = 9). A mean value regression curve was obtained from fitted value regression  31   ) in the different tree organs or soil layers by their corresponding biomass or surface density (kg dw m -2 ) respectively. For tree compartments, 137 Cs concentrations were not found to be dependent upon DBH (data not shown), and the mean values of the 137 Cs concentrations were used.
Conclusion This monitoring, carried out three vegetation periods after the FDNPP accident, produced a detailed dataset on  137  Cs concentrations and inventories in forests (trees and soil components). The sampling strategy (9 trees and 7 forest floor locations) was implemented to integrate the spatial variability occurring in two forest stands of different ages. Our results showed that the forest floor exhibited around 85% of the 137 Cs inventories and is the major contamination reservoir three years after the accident. For the young stand (YC) with the highest initial interception, major contamination is present in the organic layer whereas on the MC stand the main contamination is present in the first mineral layer (0-3 cm). These results suggest that a delay in vertical  137  Cs migration in YC forest soil occurred due to (1) a higher initial interception and therefore a lower initial deposit on the forest floor and (2) a thicker organic layer.
Acknowledgements This work was possible thanks to the  (French)  State financial support managed by the  Agence Nationale de la Recherche , allocated in the " Investissements d' Avenir" framework program  under reference  ANR-11-RSNR-0002. The authors wish to thank  D. Orjollet  ( IRSN ) and  Dr T. Takase  ( IER ) for the gamma measurements. The authors are grateful also to  Dr T. Kajimoto  and  Dr S. Kaneko  from  FFPRI  for their help in understanding Japanese publications.
The associations between the disordered eating indexes and the exposure variables (CTQ dimensions) were quantified with odds ratios (OR) and their 95% confidence interval (CI) (Table  2 ). Sociodemographic and clinical variables associated with at least one of the disordered eating indexes (with p < 0. 10) were included in the logistic regression models to estimate adjusted ORs for exposure variables. When appropriate (i. e. when a CTQ dimension and the diagnostic type were significantly associated with an outcome variable), the interaction terms were tested using Wald χ 2 test yielded by the logistic regression model. The significance level was set at p < 0. 05. All analyses were performed using SAS statistical software (version 9. 4; SAS, Inc. , Cary, NC, USA).
The main questions taken from ISAAC were for adults: Have you ever had asthma? Have you ever had wheezing or whistling in the chest at any time in the past? Have you ever had a problem with sneezing, or a runny, or blocked nose when you DID NOT have a cold or the flu? For children: Has your child ever had asthma? Has your child ever had wheezing or whistling in the chest at any time in the past? Has your child ever had a problem with sneezing, or a runny, or blocked nose when he/she DID NOT have a cold or the flu? Smoking was assessed according to three categories never smokers, current smokers, and former smokers (individuals who had stopped smoking at least one month prior to the examination). Due to their limited numbers, current and former smokers were pooled in the analysis. Data collected during a face-to-face interview focused on the household environmental characteristics, on the individual characteristics of each household member, and on respiratory health data: asthma confirmed by a health professional, wheezing, out-of-breath sensation, cough, nocturnal awakening by a fit of coughing, cough without fever, and ocular or nasal irritation.
Data Analysis. Data were analyzed with the SAS 9. 3 software. Descriptive statistics were carried out to describe the background characteristics of the study sample. Respiratory symptoms were described and compared between the two areas. Any difference highlighted in the univariate analysis ( < 0. 05) was considered and systematically included in the multivariate analysis. Categorical variables were compared in the two areas using Fisher exact test, whereas quantitative variables were compared with Chi2 tests. Logistic regressions were adjusted for main potential confounders: age, sex, education level, smoking, and body mass index. Separate analyses were performed in children and adults.
On the contrary, the higher rate of ocular and nasal symptoms in the desert area could be due to exposure to sand. The main strength of our study is the high comparability of the two populations according to their sociodemographic characteristics. Indeed, they differed essentially in their residential environmental and agricultural characteristics. In addition, our survey collected respiratory data at the individual level through face-to-face interviews, thereby avoiding the bias encountered in ecological studies in which some major factors like tobacco smoke cannot be taken into account. Despite the intensive training of the investigators and their integration in the community, we cannot rule out that cultural or linguistic specificities may have interfered with the understanding of the questions.
Introduction: Parenteral anticoagulants may improve outcomes in patients with cancer by reducing risk of venous thromboembolic disease and through a direct antitumour effect. Study-level systematic reviews indicate a reduction in venous thromboembolism and provide moderate confidence that a small survival benefit exists. It remains unclear if any patient subgroups experience potential benefits. Methods and analysis: First, we will perform a comprehensive systematic search of MEDLINE, EMBASE and The Cochrane Library, hand search scientific conference abstracts and check clinical trials registries for randomised control trials of participants with solid cancers who are administered parenteral anticoagulants. We anticipate identifying at least 15 trials, exceeding 9000 participants. Second, we will perform an individual participant data metaanalysis to explore the magnitude of survival benefit and address whether subgroups of patients are more likely to benefit from parenteral anticoagulants.
Literature search We will update our 2011 search of the following electronic bibliographic databases:  12  MEDLINE, EMBASE, The Cochrane Library (including Cochrane Central Register of Controlled Trials/CENTRAL, Clinical Trials, DARE and NHS EED). A professional librarian in collaboration with content, clinical and methodology experts will develop specific search strategies. There will be neither date nor language restrictions. We will also review references of identified studies and narrative review articles, and update our search of conference proceedings (conference proceedings of the American Society of Clinical Oncology and of the American Society of Hematology). We will also use the related article feature in PubMed to identify additional articles.
31 Disagreements will be resolved by consensus and, when needed, with the help of a third reviewer. We will record reasons for exclusion. We will contact authors of all included trials by email, fax or telephone, and invite them to share their data. Specifically, we will request the study protocol, a copy of the case report forms and the corresponding dataset with all patient identifiers (eg, names) removed. In addition to the study protocol and complete analysis plans, a detailed list of all variables of interest will be provided to contacted trialists in order to maintain analytical transparency, to avoid data driven analysis and to encourage apprehensive authors to share relevant trial data, excluding sensitive or unnecessary information from being disseminated. Data should include important demographic variables that are required for the analysis and, if possible, biomarker information.
Assessment of risk of bias and overall quality of evidence Two review authors will assess, in duplicate and independently, the risk of bias in the included trials, using a standardised form. They will compare their results and resolve disagreements by discussion or with the help of a third reviewer. We will use the following criteria to assess the risk of bias: Allocation concealment; blinding of participants, healthcare providers, data collectors, outcome adjudicators, and data analysts; proportion of participants with available data; and stopping early for benefit. We will assess selective outcome and other reporting bias by comparing the list of outcomes in published protocols and in the methods section to the outcomes reported in the published paper. We will assess the risk of bias and overall quality of evidence in the study-level meta-analysis as an initial approach.
We will generate an inverted funnel plot for each comparison to check for possible publication bias. We will assess statistical heterogeneity by calculating the Q χ 2 and its p value, and the I 2. We will record and report the sponsorship of the studies (whether sponsored by a forprofit or not-for-profit organisation or government agency), and assess financial and intellectual conflicts of interest. We will prepare GRADE evidence profiles to summarise the information about the treatment effects following the GRADE approach  17  to provide support for decision makers, using our GRADE profiler software. 18 For this purpose, we will expand the existing GRADE methodological framework to incorporate assessing the quality of evidence coming from IPDMA. For that, we will assess publication bias and heterogeneity by using study-level identifiers.
Datasets and data extraction The primary purpose of the project is to conduct an IPDMA; if any investigators are unable or unwilling to provide us with their database we will compare, in a secondary analysis, the results using study-level data, in order to include all trials. We will request trial protocols, final trial reports and unedited databases including all randomised patients. Two review authors will independently extract individual participant data and aggregate level data in duplicate using standardised prepiloted data extraction forms. They will compare their results and resolve disagreements by discussion or with the help of a third reviewer. Data of interest include demographic, intervention and outcome data. Trialists will be assured that their data will be treated confidentially.
All data sets received will be stored in a secure location in their original formats. Prior to data extraction, we will create a list of baseline variables (eg, age, sex, date of cancer diagnosis, site of primary cancer) and outcome variables (eg, mortality, bleeding, DVT, PE) to be used in the IPDMA. We will then try to identify each of these variables in each of the trial data sets. During this stage, we will check definitions of variables in the individual trials and, if necessary, create our own definitions to ensure commonality of terms between trials. We will then adapt or recalculate some of the original variables to ensure consistency of definitions. We will request and extract data about all outcomes of interest necessary to conduct analyses following the intention-to-treat (ITT) principle.
All data sets will be worked on separately in order to standardise variable names and value labels across trials. Data will initially be entered into an Excel database and converted to SAS for analysis. Finally, we will collate data sets and assign a trial variable with a code for each trial. For each study, relevant descriptive data of all enrolled patients will be presented, including demographic data (eg, age and gender), underlying diagnosis (type of cancer and stage), other baseline clinical and laboratory data, including defining criteria for the stage of their disease, other blood markers of inflammation (eg, D-dimer and P-selectin), results of diagnostic tests and prognostic scores or comorbidities. We will match data after recoding of variables into common variables. The percentage of missing data in analyses relevant variables will be calculated and Little's method will be used to assess the missing completely at random (MCAR) assumption in the variable with at least 5% of missing data.