dataesr/scientific-paragraphs-categorization huggingface dataset:
Data quality and validation Data quality and validation are key factors in determining whether EHRs might be suitable data sources in clinical trials. Concerns about coding inaccuracies or bias introduced by selection of codes driven by billing incentives rather than clinical care may be diminished when healthcare providers enter data directly into the EHRs or when EHRs are used throughout all areas of the health-system, but such systems have not yet been widely implemented  [42]. Excessive or busy workloads may also contribute to errors in clinician data entry  [43]. Indeed, errors in EHRs have been reported  [43] [44] [45]. Complete data capture is also a critical aspect of using EHRs for clinical research, particularly if EHRs are used for endpoint ascertainment or SAE collection. Complete data capture can be a major barrier in regions, where patients receive care from different providers or hospitals operating in different EHR systems that are not linked.
Consistent, validated methods for assessing data quality and completeness have not yet been adopted  [46] , but validation is a critical factor for the regulatory acceptance of EHR data. Proposed validation approaches include using both an eCRF and EHRs in a study in parallel and comparing results using the two data collection methods. This approach will require collaborative efforts to embed EHR substudies in large cardiovascular studies conducted by several sponsors. Assessing selected outcomes of interest from several EHR-based trials to compare different methodologies with an agreed statistical framework will be required to gauge precision of data collection via EHRs. A hybrid approach has also been proposed, where the EHR is used to identify study endpoints (e. g. , death, hospitalization, myocardial infarction, and cancer), followed by adjudication and validation of EHR findings using clinical data (e.
g. , allcause or cause-specific mortality) that are less susceptible to bias or subjective interpretation. As noted above, mortality may be incompletely captured in EHRs, particularly if patients die outside of the hospital, or at another institution using a non-integrated EHR. Thus, methods to supplement endpoint ascertainment in the EHR may be necessary if data completeness is uncertain. Standardized endpoint definitions based on the EHR should be included in the study protocol and analysis plan. A narrow set of data elements for auditing should be prospectively defined to ensure the required variables which are contained in the EHR. Early interaction between sponsors, clinical investigators, and regulators is recommended to enable robust designs for clinical trials aiming to use EHRs for endpoint ascertainment.
Timely access to electronic health record data The potential for delays in data access is an important consideration when EHRs are used in clinical trials. EHRs may contain data originally collected as free text that was later coded for the EHR. Thus, coded information may not be available for patient identification/recruitment during the admission. Similarly, coding may occur weeks or months after discharge. In nationally integrated systems, data availability may also be delayed. These delays may be critical depending on the purpose of data extracted from the EHR (e. g. , SAE reporting, source data, or endpoints in a time-sensitive study).
Acknowledgments This paper was generated from discussions during a cardiovascular round table (CRT) Workshop organized on 23-24 April 2015 by the  European Society of Cardiology (ESC). The CRT is a strategic forum for high-level dialogues between academia, regulators, industry, and  ESC  leadership to identify and discuss key strategic issues for the future of cardiovascular health in Europe and other parts of the world. We acknowledge  Colin Freer  for his participation in the meeting. This article reflects the views of the authors and should not be construed to represent FDA's views or policies. The opinions expressed in this paper are those of the authors and cannot be interpreted as the opinion of any of the organizations that employ the authors. MRC's salary is supported by the  National Institute for Health Research (NIHR) Cardiovascular Biomedical Research Unit at the Royal Brompton Hospital, London, UK.
Patients and methods The study was conducted over a 6-month period (1 December 2008 to 1 July 2009) in two intensive care units: A 10-bed medical intensive care unit (ICU) (Groupe Hospitalier Pitié-Salpêtrière, Paris) and a 16-bed medical and surgical ICU (Hôpital Saint-Eloi, Montpellier). The study was approved by the "Comité de Protection des Personnes Sud-Méditerrannée II", Montpellier, France. All patients or their relatives provided written informed consent to participate. Data from this cohort have been presented in previously published studies  [3, 5]. This study was registered in the US National Institutes of Health clinical trials registry (clinicaltrials. gov NCT00786526).
The endotracheal tube was then occluded, and bilateral anterolateral magnetic stimulation was performed. The absence of active respiratory efforts in response to stimulation was determined by checking the stability of the airway pressure signal. Measurements were repeated at least three times and were performed by the two same operators in each centre. Stimulations were always performed by the same two operators in each centre. Ptr,stim was defined as the amplitude of the negative pressure wave following stimulation, determined from baseline to peak. It was measured at the proximal end of the endotracheal tube, using a linear differential pressure transducer (MP45 ± 100 cmH 2 O, Validyne, Northridge, CA, USA). The pressure signal was sampled and digitized at 128 Hz (MP30, Biopac Systems, Santa Barbara, CA, USA or Powerlab, AD Instruments, Bella Vista, Australia) for subsequent data analysis.
Behavioral Analysis and Coding Data regarding multimodal (visual and acoustic) behaviors and dogs' locations were coded using the Actogram Kronos software (Octarés Edition). For each dog, overlapping behaviors (i. e. not mutually exclusive) were defined with or without movements  [7, 32] : • Gazing at owner: the dog's head and nose were oriented towards the owner's face; • Gazing at food: the dog's head and nose were oriented towards the food; • Gaze alternation between the owner and the food: gaze at the owner's face followed by gaze at the food (or vice-versa) within 2 seconds; • Gazing upwards: the dog's head and nose were oriented upwards. • Silent mouth licking: the dog displayed non-sonorous (silent) mouth licking; • Sonorous mouth licking: the dog displayed sonorous (noisy, audible on video recording) mouth licking; • Vocalization: the dog barked and/or whined; • Contact with owner: the dog touched the owner with any part of its body.
Data Analyses For the following variables, the duration and frequency of Gazing at owner, Gazing at food, the time spent in Food area and the number of Gaze alternation between the owner and the food, a first GEE model (Generalized Estimated Equation) was used to evaluate the demographic effects, gender and age, and the order of presentation of experimental conditions. The first three trials as well as the last three ones were pooled to test the order effect. Once established that these factors did not significantly affect target behaviors, each variable was analyzed with a GEE model including experimental condition as the fixed effect, and, the Wald statistic was used to test the specific hypotheses of this study (detailed bellow). The number of Gaze alternation was considered as a counting variable in the GEE model.
General information The SAS software, version 9. 2 (SAS Institute Inc. , Cary, NC, USA) was used for all statistical analyses and a 5% significance level was applied. All tests were two-tailed. A second naïve observer independently coded 36% of the sample (chosen randomly) and Kendall's concordance coefficient (W) was calculated by pooling data for Gazing downwards, Visually following and Eyes closed conditions. Inter-observer agreement was assessed for the following variables: duration of Gazing at owner (W = 0. 95), Gazing at food (W = 0. 88) and the time spent in Food area (W = 0. 98). Results indicated a good agreement between raters.
The research procedures were reviewed and approved by the ethical committee of the University Hospital of Kremlin-Bicêtre (Paris, France) according to the principles embodied in the Declaration of Helsinki. All participants provided informed consent. Briefly, 9,294 community dwellers, initially noninstitutionalized, aged ≥ 65 years were randomly selected from the electoral rolls in three French Cities: Bordeaux (n = 2,104), Dijon (n = 4,931), Montpellier (n = 2,259). Subjects were evaluated at home at the initial visit and at two years interval for a period of 12 years. At each visit, a psychologist administered a questionnaire on sociodemographic data and a comprehensive neuropsychological assessment.
Dementia cases A standardized three-step procedure was used to diagnose cases of dementia  (8). First, screening was based on a comprehensive neuropsychological examination by trained neuropsychologists. Data on activities of daily living, severity of cognitive disorders, and, where possible, magnetic resonance images or computed tomography scans were collected. Second, individuals who met dementia criteria were seen at home by a neurologist who confirmed or rejected the diagnosis. Finally, all available information was reviewed by an independent of specialized neurologists who applied the following criteria for each etiological category: NINCDS-ADRDA criteria for AD  (9) , NINDS-AIREN criteria for vascular dementia  (10) , standardized clinical criteria for fronto-temporal dementia  (11) , Lewy body disease  (12) , and a history of Parkinson disease for Parkinson dementia.
Sample Characteristics This study only considered the 2,104 subjects from the center of Bordeaux and excluded the following participants: 77 prevalent cases of dementia at baseline, 205 housewives, 282 subjects with missing data for the age at retirement and 339 for the number of working years. Of the remaining 1,658 retired subjects, 40 subjects diagnosed within 10 years after retirement were excluded from the sample. Thus, the final study sample included 1,618 subjects. After 12 years follow-up, 253 incident dementia cases (15. 6%) were reported. Table  1  displays baseline characteristics of the sample. Participants were more often women, more educated and a large percentage retired at the age of 60 (43. 5%), which is the modal age of retirement. About 5% of the participants retired after age 65 and more than one third have worked for more than 40 years.
Acknowledgments: The 3C study was conducted under a partnership agreement between the  "Institut National de la Santé et de la Recherche Médicale" (INSERM) , the " Institut de Santé Publique et Développement  of the  Victor Segalen-Bordeaux2 University ", and " Sanofi-Aventis ". The  Fondation pour la Recherche Médicale  funded the preparation and initiation of the study. The 3C study is also supported by the  Caisse Nationale Maladie des Travailleurs Salariés ,  Direction Générale de la Santé ,  Mutuelle Générale de l'Education Nationale, Institut of the Longévité, Regional Councils of Aquitaine and Bourgogne, Fondation de France  , and  Ministry of Research-INSERM Program "Cohortes et Collections de données biologiques". Catherine Grotz  is supported by the  Fund for Scientific Research (F.
Data reduction and statistical analysis The mean values of the COP position in the anteroposterior direction during the muscle vibration trials were calculated over two periods: the 10 s preceding and the 20 s during muscle vibration. The last 10 s were not Fig. 1  Picture of the bootee used in the experiment, superimposed on the foot anatomical structures. The bootee consists of a soft slipper including a tightening strap around the malleoli. The strap is connected to a lateral band crossing the ankle joint and following the main physiological structures (peroneal tendons, lateral hindfoot ligament). When movements occur at the hindfoot, a tension force is created in the band, which stretches and compresses the skin underneath analyzed because they corresponded to the participant's adaptation to the cessation of vibration.
Palmerini  [19]  applied PCA to search for a subset of variables relevant for three phases of the TUG, sit-to-stand, walking and stand-to-sit. Of the initial 28 variables of healthy adults based on accelerometer signals embedded in a smartphone, a reduced set of twelve variables was extracted using PCA, but these analyses were not used to stratify participants by age and the device also operated without a gyroscope. iTUG has previously been used for patient stratification. A linear discriminant analysis of iTUG data stratified nearly 80% of healthy and early-mild Parkinson's patients correctly, based on mediolateral (ML) and vertical Jerk during turning and anterior-posterior root mean square (RMS) during the sit-to-walk phase  [25]. As compared with TUG duration measured with a stop watch, a binary logistic regression analysis of a subset of three variables (jerk of the sit-tostand, average step duration, standard deviation (STD) of the overall performance) was more accurate in classifying non-fallers and fallers  [23].
A pattern matching k-NN algorithm was also effective in distinguishing old adults with a low and high fall risk based on the RMS of the vertical acceleration during walking, the amplitude of the yaw signal during turning and the time to complete the test. Sit-to-stand and stand-to-sit related variables were not included in the classification  [28]. Overall these studies show that a subset of parameters of the iTUG could classify certain type of patients. The current and sporadic evidence for using iTUG as a classification tool could be generalized and broadened by providing a normative database that characterizes a set of statistically selected variables for the postural and ambulatory elements of iTUG. Such data can then be used to assess the effects of natural aging and could serve as a basis for the identification of patients with mobility disability  [10, 19, 23, 25].
Therefore, the first aim of our study was to identify iTUG variables that are associated changes in performances of the iTUG across the adult lifespan. Secondly after identification of the most important iTUG variables we assessed if these variables could accurately discriminate two age groups one of age 18-45 and one of age 46-75 year. Because the onset of decline in of muscle mass and muscle function starts around age 40-45 year, we chose a cut-off value of group division at age 45  [29] [30] [31] [32]. We combined a wavelet analysis algorithm (to identify phases of iTUG) with a phase detection algorithm based on accelerometer and gyroscope data and applied statistical analyses to specify variables that could effectively classify healthy young versus old adults. To this aim, first we used a Partial Least Square analysis (PLS), a method that combines dimensionality reduction and regression, to identify the variables of the iTUG that are sensitive to age.
Methods Participants Fifty-nine healthy adults participated in the study (45±18 years, range of age: 18 to 75, 46% male) and served as a basis for two age groups: 18-45 (28±7 years, n = 28, 61% male; weight = 75. 4 ±7. 6 kg; length -178±11. 28 m) and 46-75 (62±8 years, n = 31, 32% male; weight = 73. 1±13. 3 kg; length = 169. 5±9. 4 m). All subject were healthy and active. Participants were asked to report the number of hours a week they engaged in physical activity during a typical week (e. g. , tennis, dance, hiking, yoga). Participants in the younger group were on average 4. 8±2. 0 hours active a week and participants in the older group 2. 9±2. 2 hours a week. Data of four participants (3 young; 1 old) was excluded from one of the two trials they performed, because the data was not correctly recorded due to a corrupt memory card.
Instrumentation and procedure Trunk accelerations were measured during the TUG with an Inertial Measurement Unit (IMU; DynaPort1 hybrid unit (56x61x15 mm, 54 g; McRoberts BV, The Hague, the Netherlands). The unit consists of a tri-axial accelerometer and gyroscope sensor (100 Hz sample frequency). Data was stored on a SD card for off-line analysis of the signals. The IMU was fixed with an elastic belt at the level of lumbar segment L3 over the participant's clothes. Participants performed the iTUG two times. The iTUG consisted of standing up from a chair without the use of the arms, walking 7 m, turning around a pion, walking 7 m back to the chair, and sitting down without the use of the arms. Participants were instructed to perform this task as fast as possible without running. Since the iTUG was performed in the context of a larger study the TUG trials were randomized with three other gait tests.
Standing-up and sitting down The pitch signal was analysed with a db5 mother wavelet and its reconstruction was based on the level 4 approximation (4A). Thereafter, peaks (Fig 1 ) in the reconstructed signal were detected using a peak detection algorithm 'findpeaks' of the signal toolbox of Matlab, which searches for local maxima in the signal. Fig 1 presents the phases of standing-up and sittingdown. From 1a to 1b, the subject moves the trunk forward in preparation for rising from the chair. Subsequently, from 1b-1c the trunk is moved backward until standing upright. In the sitting down phase (3a-3c) the pattern is repeated.
Variables calculated from the iTUG phases We calculated the same variables for phases of iTUG that have been reported in the literature  [10, 14, 19, 22, 23, 25, 28, 40]. First, the duration for each phase was calculated. Second, we calculated the amplitude, range of the movement, variability and smoothness of the movement for sit-to-stand, stand-to-sit, and for the two turns. Data for the two walking phases were combined. From foot contacts, step-related variables (e. g. , stride time, number of steps) were calculated. From the ML and AP acceleration signals, we computed measures of stability and smoothness of gait. Altogether, we calculated 72 variables for the Partial Least Square (PLS) analysis (Table  1 ). Outcome measures were expressed in absolute values, being positive or negative signs according to the direction of turn.
PLS analyses A Partial Least Squares (PLS) regression analysis was applied to determine the iTUG variables that were related to age (PLS-Toolbox 8. 1 for Matlab, Eigenvector Research Inc. ). PLS analysis combines PCA with regression analysis. Compared with step-wise regression or structural equation models, PLS methods can handle a larger set of independent variables with lower number of observations. Moreover, multivariate PLS regression allows the modelling of multiple responses, while dealing with multicollinearity  [41] , which is often present in motion data, including walking. The general aim of the PLS analysis is to define a maximum covariance model and explain the relationship between the iTUG variables (X-matrix, predictors) and age (Y-matrix, responses). In other words, successive orthogonal factors are chosen that maximize the covariance between each X-score and the corresponding Y-score to find a model that best predicts age with a selected number of iTUG variables.
To assess the PLS model, several outcomes were derived. First, the goodness of fit (R2) of the model was determined. The R2 explains how well the model fits the data and is calculated as follows: R2 k ¼ 1 À RSS k TSS ð3Þ The R2 is defined by the residual sum of squares of the k th LV and the total sum of squares (TSS). Next, the weights of the PLS model were assessed. They illustrate the relationship between iTUG variables and the participant's age, with respect to the individual LV. The weights describe the importance of iTUG variables and age on the model for individual LV. If they are near zero for all identified LVs than they add little to the model. To identify which iTUG variables are of importance to the model the regression coefficients of the PLS matrix and the Variable Importance for Projection (VIP) are evaluated.
PLS-DA analysis To determine the classification power of the iTUG variables identified in the first PLS analysis a PLS-DA was performed on the dataset of the second iTUG trial. The iTUG variables selected from the first PLS analysis thus formed the X-matrix. For the discriminant analysis, the participants were separated into two age groups, one with age 18-45 and one group with an age of 46-75 years. Based on the PLS-DA a Receiver Operating Characteristic (ROC) curve was constructed. This curve includes both the true positive rate (sensitivity) and false positive rate (specificity) of the model. Each point on the ROC-curve represents a sensitivity/specificity pair, which is related to a threshold that determines the optimal boundary between younger and older adults in the classification. The Area Under the Curve (AUC) is an indicator of the classification power of the model.
e. excluding voices, birds, human activities, wind, fountains, helicopters, etc. ). Participatory noise sensing results however in a serious challenge for both the public authorities and research  [6]. Firstly, the accuracy and relevance of the collected data stand for both a key point and the main current limitation. Secondly, the information fed back to the user is for now mainly limited to an historic of the noise data measured, which could be usefully enriched by indicators dedicated to the understanding of the noise environment that surrounds the user (e. g. Repartition of Noise Exposure (RNE), Sound Exposure Level (SEL), etc. ). The present work is incorporated within the framework of the European ENERGIC-OD project² (European Network for Redistributing Geospatial Information to user Communities -Open Data), which aims at addressing the issues of both the interoperability and accessibility to geospatial datasets by deploying a set of Virtual Hubs that include INSPIRE-compliant systems and Copernicus/GMES (Global Monitoring for Environment and Security) services.
In this paper, a system is described that consists in enhancing citizens participatory sensing within 2 Website: http://www. energic-od. eu/. the context of noise exposure assessment. The originality of the proposed approach stands in the full standard compliant Spatial Data Infrastructure (SDI), named OnoM@p, that embraces a specially developed smartphone application based on android SDK. This application can report back to the contributor useful noise exposure information thanks to especially designed outsourced data treatments. These latter processes control the accuracy of the provided data and give access to a new set of indicators based on both geographical and demographic data. The section 2 presents the pro and cons of the classical noise mapping approaches. The section 3 highlights the possibilities offered by participatory noise sensing alternatives, and reviews the existing dedicated tools.
The NoiseSpy application is part of the MobSens project that also embraces three other mobile phone applications dedicated to health, social and air pollution sensing  [58]. The NoizCrowd application, developed within the framework of the BioMPE ("Bio-inspired Monitoring of Pervasive Environments") project¹³ (2010-2013)  [59] , produces noise data in RDF (Resource Descrip- tion Framework) format that enables to link the generated data with other datasets in the Linked Open Data cloud¹⁴  [60]. In addition, interpolation and noise propagation models are involved in order to generate missing data, and the Google Map application is used to display extra information at noise measurements locations. Otherwise, the NoiseWatch application¹⁵ is integrated into a threelevel assessment structure whose topics are modelling, measuring and citizen rating  [61].