dataesr/scientific-paragraphs-categorization huggingface dataset:
demonstrated that GS provided more relevant results that PubMed, although the difference was not significant (p=0. 116) [28]. GS has been reported to be less precise than PubMed, since it retrieves hundreds or thousands of documents, most of them being irrelevant  [29, 30]. Nevertheless, we should not overestimate the precision of PubMed in real life since Precision and recall of a search in a database is highly dependent on the skills of the user  [10]. Many of them overestimate the quality of their searching performance, and experienced reference librarians typically retrieve about twice as many citations as do less experienced users  [31, 32]. Although this was not the purpose of our study, we tried to assess the precision of GS for some of the clinical questions that were studied by the systematic reviews.
Sensors are using the ZigBee communication protocol on a wireless sensor network based on Crossbow's IRIS mote platform. A Crossbow node is connected via serial port to the Debian machine, serving as gateway. The communication with other devices in the environment uses Bluetooth for residents' embedded speakers, a client-server communication over Wi-Fi for the nursing console (Windows 7 machine with touchscreen) or 3G for the nurses' smartphones (Samsung Galaxy S2 with Android 2. 3 and Apple iPhone 4 with iOS 5). Within the system, a software platform, assuring the context aware reasoning and the provision of assistive services for the patients, was developed based on human and technical requirements gathered from the pre-deployment analysis, and installed on the Debian compact machine of each room.
Data gathering & performance evaluation In order to evaluate the performance of our system, and as we have committed ourselves not to use video recording to preserve the privacy of the nursing home's residents and caregivers, we have chosen to rely on log-sheets filled by the caregivers and compared with our system logs. We have collected all system log data during our trial period of 14 months and extracted meaningful information concerning sensors states and patients' context, then compared them with the caregivers' log-sheets. Log-sheets provide information about the ground-truth: caregivers were asked to fill them up with hourly information about patients' location, patients' abnormal behaviours and possible remarks. Table  4  is a sample of blank caregivers' logsheet which was used for our ground truth data collection through data entry into a database.
Conclusion In this paper, we have presented an approach to deploy assistive technologies, that starts from a pre-deployment analysis on the field in a collaborating nursing home with implication of professional caregivers in order to gather human and technical requirements. We have then provided a high-level overview of a software platform that responds to these requirements which was deployed in our collaborating nursing home for 14 months with 8 patients and 2 caregivers. An analysis of the data collected during this deployment allowed us to demonstrate the ability of the proposed solution to detect abnormal behaviours and to evaluate the performance of the system with the help of caregivers. The deployment also helped us to extract a set of lessons learned that will serve for further research work in the domain.
0 cm. The length of the leg and the circumferences of the limbs were measured using a non-elastic measuring (cm) tape (KaWe CE, Kirchner und Welhelm, Germany) to the nearest 0. 1 cm. The circumference of the upper arm was measured at mid-arm, the circumference of the thigh was taken at mid-thigh and the circumference of the calf was measured at mid-calf. All skin-fold data were obtained using a skin-fold calliper (GPM-Hautfaltenmessgerät, Siber & Hegner, Zurich, Switzerland) and recorded to the nearest 0. 2 mm. The skin-fold caliper measures with a pressure of 0. 1 MegaPascal (Mpa) ± 5% over the whole measuring range. The skin-fold measurements were taken once for all eight skin-fold sites, and then the procedures were repeated twice more by the same investigator. The mean of the three measurements was used for calculation.
Statistical Analysis: Data were analysed using SPSS software version 15 (SPSS Inc. , Chicago, USA). Prior to analysis, data were checked for distribution of normality and are presented as mean ± standard deviation (SD). The coefficient of variation (CV) of performance (CV% = 100 × SD/mean) was calculated. The coefficient of variation describes the magnitude sample values and the variation within them. Data for the female and male half-marathoners were compared using the Mann-Whitney U-test. To investigate a potential association between anthropometric and training characteristics with race time, in a first step, the relationship between race time as the dependent variable and the variables of age, anthropometry, training and previous experience was investigated using bivariate Pearson correlation analysis.
Methods Consecutive children were addressed to the cardiology unit of Military Hospital Bouffard (Djibouti) for cardiac symptoms or cardiac murmur, and were prospectively studied over a 2 two-year period (January 2009 -December 2010). Their age ranged between 1 month and 15year-old. Patients were addressed from local medical centres, other hospitals in Djibouti or were directly hospitalized in our centre. Clinical data included age, gender, symptoms (such as breathlessness, repeated chest infections) and clinical findings (cardiac murmur, cyanosis). Human Immunodeficiency Virus (HIV) status was noted. Children with CHD and children with other inherited or acquired cardiomyopathy were included. Patent ductus arteriosus (PDA) and patent foramen ovale in children under three month-old were not considered as CHD except in case of a large PDA.
Echocardiographies of all patients were performed according to the recommendations of current guidelines with a Siemens-Acusson CV 70 cardiovascular system (P 4-2 MHz adult transducer and P 9-4 MHz pediatric transducer)  [5]. Results from other imaging examinations, including Computed Tomography Scanner were also obtained. Outcome and management pathway were noted. There was no possibility for cardiac surgery in Djibouti. When it was required and possible, CHD were sent abroad for cardiac surgery. Type of surgery and outcome after surgery were reported. The group of survivors at the end of follow up was compared to the group of non-survivors at the end of follow up. Mortality risk factors were analyzed. For statistical analysis, Microsoft Excel® software was used. Data were expressed as mean ± standard deviation (range).
Veillard and colleagues describe experience with international benchmarking efforts, and demonstrate that there remains much to be done to secure appropriate policy attention  [19]. Butler and colleagues describe the international cancer benchmarking initiative, which has had an especially strong impact on policy in some countries  [20]. Cacace and colleagues caution against weak comparative studies, and call for the development of criteria against which to assess the quality of comparisons  [21]. The strongest theme emerging from the papers is the need for careful attention to the design, collection, governance, linkage and dissemination of data. International efforts are needed to secure consensus on data specification and quality standards. Data should be collected in a timely and representative fashion.
The results from the Mini International Neuropsychiatric Interview were used to confirm the diagnosis of schizophrenia given by a psychiatrist  [28]. The assistant then described the study, responded to any questions the patients had and obtained written informed consent. Evaluations were performed during face-to-face interviews by the research assistant in the offices of the mobile mental health outreach teams, which were located in the downtown area of each city. Participants received twenty-one euros of food coupons for each interview. The patient completed the QoL questionnaire independently or asked for assistance to complete all or part of the questionnaire. All data were collected using netbook computer-assisted interviewing (small inexpensive laptop computers) and transferred to a highly secure central database without using the internet (EpiConcept®).
Data collection The following data were collected from patients: 1. Socio-demographic information: gender, age and marital status. 2. Clinical characteristics: Mental Health was assessed using the Modified Colorado Symptom Index (MCSI), which has been validated in homeless individuals  [29]. The MCSI contains 14 items that ask about how often in the past month an individual has experienced a variety of mental health symptoms, including loneliness, depression, anxiety, and paranoia. An index score for this scale is calculated by summing each response. Higher scores indicate a higher likelihood of mental health problems. Recovery was assessed using the Recovery Assessment Scale (RAS)  [30] , which measures various aspects of recovery from the perspective of the consumer, with a particular emphasis on hope and self-determination.
However, given the magnitude of the effect sizes in our study, it is arguable that our results would remain satisfactory. Fourth, an important perspective of our work would be to determine whether our findings depend on site, especially because the study procedures were not standardized between sites. This issue should be explored in future studies on larger samples using in particular recent suitability indices proposed to compare psychometric properties between samples  [21, 22, 32]. Finally, important data concerning external validity were not collected in our study because some assessments are too complex to conduct in this population (e. g. , the determination of disease duration and other clinical scales, such as the Positive and Negative Syndrome Scale for symptomatology). Future studies should specifically address these issues.
Prospective identification of adverse events by physicians All adverse events occurring in the post-emergency unit are recorded in a dedicated file in the medical information systems program database (Programme de Médicalisation des Systèmes d'Information, PMSI). PMSI is a generalized hospital database in which medical data related to hospitalizations are entered. Each hospitalization corresponded to one file card in the PMSI, which means that the number of patients recorded in the PSMI corresponds to the number of patients admitted to the hospital. These data are used in the calculation of hospital funding and are now used for pharmaco-epidemiological studies to assess morbidity  (7, 8). In line with previous studies (9-13), we defined an adverse events as an event that i) was a source of harm to the patient, and ii) was due to medical management rather than to the disease process itself.
Since 2009, codes for extra and intra-hospital adverse events have been added in the PMSI database specifically for patients hospitalized in the post-emergency unit. Since that time, after discussion with the paramedical staff, senior physicians enter a prospective daily record of all adverse events that occur during hospitalization. They are searched during the daily morning staff for each in-hospital patient. A dedicated file is prospectively created to record all adverse events noted by physicians. Thus all patients presenting an adverse event identified by senior physicians working in the post-emergency unit were consecutively and prospectively recorded in the database. All the hospital adverse events codes recorded from September 1, 2009 to February 28, 2011 were included.
Data collection According to the list of patients provided by the database, we performed a retrospective data collection among medical files to complete reports and outcomes of these adverse events. Two independent investigators reviewed the corresponding medical files to check that the adverse events occurred during hospitalization. Data collected were age, sex, number of medications, comorbidities, dependency, history of hospitalization and type of adverse events. The number of drugs at admission included both chronic and acute treatments. Comorbidities were assessed by the Charlson score  (14). Dependency was defined as need for nursing care at home or living in a care home or institution before admission.
Statistical analysis The sample size was based on the feasibility of data collection and was considered sufficient to observe an incidence of 10% of adverse event occurring during hospitalization  (9). To compare cases and controls, the χ 2 test or Fisher test was performed for qualitative variables and the Wilcoxon test for quantitative variables. To identify factors associated with the occurrence of adverse events, be they drug-related or device-related, a conditional logistic regression was performed. Covariates associated at the threshold of 20% were included in the multivariate model (backward procedure, α = 5%). Variables included in the model were age, sex, number of drugs, Charlson score, dependency and hospitalization in the previous year. As we wanted to know associated factors with adverse events, backward procedure was chosen.
The angular displacements of the segments head, upper-and lower-torso, lumbar and pelvis together with the angular displacement of the platform were afterwards obtained in the frontal plane from the filtered markers' positions (Fig. 2B ). Cross-Correlation. The coupling between the periodic activation of the ES and T muscles and the platform rotation was determined using sample cross-correlation estimation  [22] : R R xy t ð Þ~1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P DxD 2 P DyD 2 q X N{t{1 n~0 x nzt y n for 0ƒtƒN{1 ð1Þ and R R xy t ð Þ~R R yx {t ð Þ for { N{1 ð Þƒtƒ{1 where R R and t denote the estimate of the correlation coefficient and the time lag between data sets x and y of length N, respectively; x representing the integrated EMG and y the angular displacement of the platform.
Note that cross-correlation was normalized so that in case x and y would have been similar, the correlation coefficient at lag 0 would have been 1. Technically, Eq. 1 was solved using a fast Fourier transform (FFT) procedure, which was more efficient than solving it directly  [22, 23]. The 100-s-long x and y data sets were first segmented into several overlapping segments that counted 2 15 points (i. e. , segments 32. 768 s long). Then, 15-bit FFT algorithm was applied to these segments to generate segments' periodograms that were averaged to obtain an 'ensemble average' power spectral density of improved statistical stability. Finally, cross-correlation was obtained by applying the inverse FFT to the cross-power spectral density function of x and y, which was obtained from the product of the FFT transforms of x and y.
2C ). In all cases, the accuracy of the time delay calculation was 10 ms, due to the sampling frequency of 100 Hz. Marker displacement. The displacements of the markers in the medial-lateral direction at the time of maximal rotation of the platform to the right and to the left were averaged over the trial to get information about the strategy of balance control. These displacements were calculated with respect to initial position before the platform start rotating, the lower the averaged displacement the less destabilized the upper body (i. e. , a good damping of the perturbation). Given that averaged medial-lateral displacement strongly depended upon the subject's upper-body height, group data were normalized by removing trends due to height  [26, 27]. Specifically, marker data sets for each group of subjects were fitted in a least-squares sense using first-order polynomialsy~a 0 za 1 x.
When ANOVAs yielded significant results, post hoc assessment was performed by Dunn's multiple comparison tests and multiple comparison correction by a step-down Bonferroni-Holm procedure. Onesample Wilcoxon signed rank tests were also used to evaluate whether time lags t differed from 0. Statistical analysis was performed using SigmaStat statistical software package v. 4. The threshold of statistical significance was set at p,0. 05. In case of multiple comparisons, this threshold applied for the whole family of comparisons and the per-comparison significance was evaluated from corrected p-values.
Data There are three sources of data for this experiment. First, we use the administrative lists of job seekers provided by ANPE to the Ministry of Labor. For each job seeker, these files provide the individual's age, postal address, the number of months spent unemployed during the current unemployment spell, the type of job being sought, and the public employment agency in charge of helping her. These registries are imperfect, because they are not updated in real time; as we will see, a number of workers who were randomized into treatment were in fact already employed at the time of randomization. A second dataset comes from private counseling firms' administrative files. In order to claim payment, these firms submitted lists of job seekers who actually entered the counseling scheme. Payment was conditional upon a job seeker filling out and signing a form, and copies of the form were reviewed to ensure that firms were not overstating the number of job seekers they were actually counseling.
We use this dataset to measure program take-up. Our third source of data are four follow-up surveys conducted 8 months, 12 months, 16 months and 20 months after random assignment. These surveys were necessary because existing administrative data do not provide a good measure of the transition from unemployment to employment; the information recorded reliably is whether someone is still registered as an official job seeker. 16 A youth who stops being registered could either have become discouraged or found a job. In addition, young job seekers do not have strong incentives to be registered with the ANPE, in particular because they are often not eligible to receive unemployment benefits. Unfortunately, administrative data on employment and wages (from the tax authority or the social security administration) cannot be linked to the experimental data for legal reasons related to confidentiality protections.
Unfortunately, looking for externalities among the rest of the unemployed population is not feasible due to data constraints. 19 However, this implies that the externalities should be larger in sectors where eligible workers form a small part of the relevant labor market. Thus, to investigate the displacement issue, we split the sample according to κ, the share of eligible workers in the sector where they are looking for a job. We compute κ as follows: when they first register at the ANPE, job seekers indicate the occupation in which they are looking for a job. There are 466 such occupation codes. Using a nationwide database of job seekers, we compute the share of skilled job seekers among all job seekers under 30 searching in this occupation. Column (1) in table 6 lists the 10 categories in which the share of skilled workers is the highest (high κ) and the 10 categories in which the share of skilled workers is the lowest (low κ), along with the corresponding shares.
Procedure The study population comprised all 1,666 students attending three middle schools in a geographical area of the Nancy urban area (410,000 inhabitants) in north-eastern France. The population studied included all students from all the middle schools (two public and one private) in this area which included 63 classes. The study protocol included: an application to participate transmitted to parents/guardians via the students (April 2010), and data collection undertaken (May-June 2010) using an anonymous self-administered questionnaire in the course of a 1 h-class period, under the research team supervision with the help of teachers (when they wished, for surveillance with no influence on the survey). The completed questionnaire was put in a sealed envelope and then in a closed box by the subject.
Acknowledgments The authors would like to thank  M. J. Chau ,  B. Kabuth ,  J. C. Vauthier ,  E. Aptel ,  I. Armand ,  B. Barraud ,  O. Causin ,  M. Verdin ,  C. Richoux ,  Y. Delacour ,  C. Grangé ,  A. Chatton ,  L. Hiroux ,  L. Collet , and the staff of the schools for their valuable help in the study. This research involved French national institute with public funds.
Individual and neighborhood variables Gender was coded as a binary variable. Age was categorized into 3 classes: 30-44; 45-59; 60-79 years old. Education was divided into 4 classes: no education (low); primary and lower secondary education (middle-low); higher secondary and lower tertiary education (middle-high); and upper tertiary education (high). Nonownership of dwelling was coded as a binary variable. Based on separate sources of data geocoded at the building level, two neighborhood socioeconomic variables were defined in buffers of 500 m of radius centered on the residence of the participants. These buffers took into account the street and road network around the residence (i. e. , the radius of 500 m was defined along the street network). The educational level of residents in the neighborhood was defined as the proportion of residents aged >25 years with an upper tertiary education (2006 Census).
Transportation noise variables The (road, rail, air) transportation noise data were provided by Bruitparif. This non-Governmental Organization is in charge of gathering published layers of noise modeled by each municipality or grouping of municipalities in the Paris metropolitan area from 2007 onwards, according to the Environmental Noise Directive  [2]. We chose to use these noise maps to characterize transportation noise exposure at the place of residence and residential neighborhood scales and to analyze the relationship with annoyance due to road traffic reported for the residential neighborhood because a high correspondence has been documented between the relationships of observed or predicted noise exposure with noise annoyance at these scales  [22]. The measurement of noise in this database is in dB(A) and is expressed with the standard European Lden and Ln indicators.
For instance in Paris, the commissioned engineering office used the EASYMAP model (SIRIATECH, Roubaix, France). This model was based on (1) the environmental noise prediction software MITHRA (Scientific and Technical Centre for Buildings, Grenoble, France), (2) the geographical information system ArcGIS (ESRI, Redlands, California, USA) and (3) the environmental management information system Drag&Fly (SIRIATECH, Roubaix, France) to generate noise calculations and noise mapping across Paris in two or three dimensions. Additional explanations (input parameters, detailed methodology) on the modeling of noise in Paris are provided in a previous article  [23]. The layer provided by the city of Paris was built from a raster with noise information on a 2 × 2 meter cell grid, at 1. 5 meter from the ground, and taking into account the distribution of buildings.
Differently, the layers provided by the other cities in the Ile-de-France region were vector files of noise points or noise lines, modeled every 2 meters at 4 meters from the ground and at 2 meters from the buildings' frontage. Despite this heterogeneity, an overall noise map was built by Bruitparif to comply with the Environmental Noise Directive Recommendations. When municipalities did not generate or release a noise database for their territory, Bruitparif completed the missing information with a noise database elaborated by government services. These institutions had to elaborate maps of road, rail, and air traffic noise that were less precise than those produced by the local authorities. Indeed, only roads with more than 6 million vehicles per year, railways with more than 60 000 trains per year, and airports with more than 50 000 movements per year were taken into account in this governmental modeling of noise.