dataesr/scientific-paragraphs-categorization huggingface dataset:
IntrOduCtIOn Biometry measurements obtained from twodimensional 2D ultrasound (2D US) images are part of the assessment of fetal development (standard care). A typical biometric dataset includes measurements of the head circumference (HC), the biparietal diameter (BPD), the abdominal circumference (AC) and the femoral length (FL). Recent studies show that the quality and reproducibility of these measurements are userdependent  1 2  and that the variability of two repeated measurements of the same parameter is probably underestimated. 3 ] [6] [7]  These strategies can be classified as semiautomated methods. In practice, semiautomated measurement software uses image recognition techniques to select the optimal positioning of measurement callipers in 2D ultrasound. Semi-automated approaches reduce the positioning variability compared Open access with user calliper placement.
Investigators Investigators in this study (obstetricians and midwives) will be trained to use the ultrasound system in a standardised fashion to secure optimal reproducibility. To limit bias induced by supervised learning  15  , at least three different operators will contribute to data collection. preliminary study A subgroup of 50 patients was enrolled in the study between 23 October 2018 and 2 May 2019 in order to formalise the acquisition procedure. The data collected were not used for the clinical assessment but contributed to the final development of the BabySize3D prototype. No changes were provided to patients' standard care.
The supplementary investigation duration will be about 5 min and will be done immediately after the standard US obstetrical examination and standard biometric measurements. All the data will be reported in a case report form on line (E-CRF). A subgroup of 50 subjects will be enrolled in an ancillary study and will undergo additional 2D standard measurements and 3D volumes acquisitions done by a second operator as a part of the research activity. As a part of the ancillary study, subjects will undergo: ► HC 2D measurement and BPD measurement (to assess interobserver reproducibility in conventional 2D head measurements) ► AC 2D measurement (to assess interobserver reproducibility in conventional 2D abdominal measurements) ► FL 2D measurement (to assess interobserver reproducibility in conventional 2D femoral measurements) ► Acquisition of a head volume (to assess interobserver reproducibility in 3D manual and 3D automated head measurements) ► Acquisition of an abdominal volume (to assess interobserver reproducibility in 3D manual and 3D automated abdominal measurements) ► Acquisition of a thigh volume (to assess interobserver reproducibility in 3D manual and 3D automated femoral measurements) The investigation duration for the ancillary study will be about 10 min and the acquisitions will be done immediately after the volume acquisitions of the main study.
StAtIStICAl dESIgn A total of 225 patients will be enrolled with the following criteria: complete 2D standard measurements and complete 3D US data sets. In this sample ► A group of 50 patients will be enrolled in order to formalise the acquisition procedure and to contribute to the final development of the investigational device (BabySize3D stand-alone software) ► A group of 175 patients will be enrolled for the investigational device evaluation: -A subgroup of 125 patients for the principal study only -A subgroup of 50 patients for both the principal and ancillary study This sample size estimation is based on the 2D reference measurements at the 50th percentile for AC and FL of the Collège Français d'Échographie Foetale (CFEF). These measurements are based on the study by Salomon et al. 15  In order to show an equivalence between 2D and 3D ultrasound of FL measurements at 22 weeks gestation with an equivalence margin of 1 mm, a mean value of 38 mm (zero difference between the two methods), an SD of 2.
42 mm, an alpha risk of 0. 05 and a power of 0. 95; 153 patients should be included. To take account of measurement difficulties and incomplete data; we increased this number by 15%. A total of 175 patients will be included in the software evaluation. Stored volumes will be retrospectively analysed. The optimal viewing plane will be identified from the 3D volume and measurements from the calliper placement. The analysis includes ► 3D automated processing with BabySize3D standalone software (3D automated measurements) ► 3D manual measurements by one of the investigators. Operators will perform measurements blinded to previous results. Fifty plans obtained by Babysize3D, the clinical specialist and the standard 2D planes will be reviewed and scored by another operator. This process will assess quality criteria and validate biometric measurements.
Rates of unacceptable planes (scoring ≤1) and suboptimal planes (1<scoring ≤ 3) will be estimated. A previously published image scoring method (table 2) will be used for the reviewing process. 16 ll analyses will be performed with R software (R Foundation for Statistical Computing, Vienna, Austria). The two-tailed significance level will be set to p<0. 05. Normally distributed continuous variables will be expressed as mean and SD, non-normally distributed data in medians and IQR and categorical variables as frequencies (percentages).
Secondary analysis Mean, SD, median, maximum and minimum for quantitative data. Qualitative data will be described by frequency and percentage. Mean comparison between parameter measurements or scores will be performed by using paired Student's t test or Mann-Whitney test according to their type. Reproducibility (intraobserver for the main study and interobserver for the ancillary study) will be evaluated by calculating the intraclass correlation coefficient with its 95% CI. This approach will be completed by a graphical representation using a Bland-Altman plot. procedures considering all data Statistical analysis will be performed on all collected data except the data from the 50 patients which will be used for the BabySize3D software development. treatment of missing data Every effort will be made to complete missing data and remaining missing data will not be reconstituted.
data management procedures The data local storage will be performed via ArchiMed database; this platform is declared to the French authority CNIL (Commission Nationale de l'Informatique et des Libertés No. 1410005). A data transfer to Philips will be done by the end of this study. For this purpose, an identification code (UID) will be associated with each exported case (2D measure-ments+2D and 3D acquisitions+rounded Gestational Age). Only authorised individuals associated with the study (investigator, sponsor, health professionals working for the study and quality controllers) will be given access to the data. All anonymised data will be kept at the investigation site responsible for conducting the study, for preventing loss from computer failure. The data collected in the E-CRF will be fully anonymised.
Monitoring plan Monitoring activities will be taken care of by the sponsor according to the ISO14155 standard "Clinical Investigation of medical device for human subjects". Monitoring procedures will be performed every 50 inclusions. The investigation site will be responsible for ► Exercising reasonable efforts to enrol the number of patients defined in this study. The investigation site may need to screen more than the defined number of patients in order to enrol the number of defined patients; ► Ensuring that informed consent is properly obtained from enrolled patients Maintaining records of enrolment for the study and any other records necessary per institutional requirements; ► Developing and maintaining data management tools; ► Ensuring that the study is conducted in accordance with the signed agreement and this clinical investigation plan, in particular performing image acquisition and measurements as defined in the aforementioned protocol; ► Collecting, assessing, reporting, and managing solicited and spontaneously reported adverse events (AE) and other unintended effects of trial interventions or trial conduct; ► Data sharing: --Anonymised images (US acquisitions) will be shared between the investigation site and Philips Research department.
Open access --All patient data will be removed from the images prior to sharing the data in this study ► Statistical analysis and summary of the study results These responsibilities fall to the investigators under the principal investigator (GA) responsibility, to the project manager (CB), to the mandated biostatistician (GH), to technical CHRU staff (regarding ArchiMed database). The sponsor will be responsible for ► Ensuring all the necessary ethic review(s) and approval(s) are obtained ► Quality control and enrolment approval (studies with incomplete acquisitions, corrupt files or missing images or measurements will not be approved) ► Providing the principal investigator with the necessary information to conduct the clinical study ► Preparing and submitting the clinical study application to the Ethical Committee.
data curation Data collected for the purpose of the study will be stored in Archimed database. The storage will be renewed every 5 years until the complete analyse and publication of results. dissemination This study will provide data on the relevance of using 3D automated measurements in standard care (feasibility, reproducibility and agreement with conventional 2D measurements). In the short term, these results will be distributed in the form of an original article. In a medium term, these results will enable the design of intention to treat randomised studies assessing the real impact on fetal screening of using artificial intelligence in reducing variability and inaccuracy in fetal measurements. rISk And bEnEfItS ASSESSMEnt potential risk related to the study The risks associated with the investigational device and the clinical procedure applied in this clinical investigation are estimated in accordance with ISO 14971; 2012 titled "Medical devices -Application of risk management to medical devices".
dISCuSSIOn This study aims at evaluating the impact of full automation of standard measurements for fetal growth monitoring and fetal size estimation. The first limitation of this study is related to the skill level of the operators that will participate in data collection. Automated fetal biometry is at a development stage, and additional data is required to train the system to reach human-level performance. The first 50 data sets to be collected will be used for machine learning. This supervised learning approach requires high-quality data. 17 These scans will be performed by expert sonographers to ensure that the data is of sufficient quality. For the second data set, the evaluation of the biometry measurements reconstructed from 3D acquisitions will be based on comparison with the 2D acquisitions, thus reflecting expert-level performance.
17 Due to this dual methodological constraint (high-level 2D quality data for assessment, and relevant measurements in 3D associated with the acquired data), it was decided to have this second set of acquisitions performed by expert sonographers as well. This choice was also strengthened by the need for global consistency in comparisons between dataset and for ethical issues (to reduce the number of operators interacting with patients, we excluded the possibility of adding non-expert investigators). However, to limit the overfitting bias 17 related to expert acquisition and variability reduction into the sample, at least three different experts investigators will perform the 3D acquisitions and the range of gestational age at the time of acquisition is extended from 16 to 30 WG with minimal restrictions on exclusion criteria (eg, coauthors choose not to exclude fetus with growth abnormality) », those aspects guarantee a realistic variability in the data set.
Literature search, study selection, and eligibility criteria A medical reference librarian was asked to execute a comprehensive literature search of the PubMed, MEDLINE, EMBASE, Web of Science, and Scopus for case series published in English from January 1, 2000 until June 13, 2018. Searched terms were: pCONUS, pCONUS2 (its newer prototype), and aneurysm. First, titles and abstracts were screened, and non-relevant and redundant articles were excluded. Full texts of the remaining studies were obtained and reviewed by two investigators (Thomas J. Sorenson and Marta Iacobucci) and only manuscripts that specifically reported data regarding the treatment of intracranial aneurysms with the pCONUS devices were included in this meta-analysis. Exclusion criteria for the full-text screen included: no relevance, not written in English, conference abstract, case reports, case series reporting fewer than five aneurysms treated, review articles, commentary or errata, withdrawn articles, and book chapters.
A summary of the search strategy is presented in Figure  2. Once the final list of articles meeting inclusion criteria was compiled, the data were abstracted from each study, including but not limited to: age of patients; number, size, morphology, and status (ruptured versus unruptured) of aneurysms; treatment details (number of devices used, technical success, complications, related morbidity/mortality); incidence of perioperative complications, morbidity, and mortality; time to last follow-up; radiological status of aneurysm at last follow-up; clinical status at last follow-up. Data were abstracted by two separate authors (Thomas J. Sorenson and Marta Iacobucci) and compared. 3].
Experimental Setup The imaging system is composed of an RGB CMOS camera (BASLER acA2000-165uc) in conjunction with an Edmund Optics camera lens (f ¼ 50 mm f∕2 -f∕22), a continuous wave white light source (OSRAM Classic 116-W 230-V light bulb) and a laptop (processor: Intel Core i5-7200U, 2. 50 GHz × 4, RAM: 15. 3 GiB); see Fig. 2. During data acquisition, the camera also acquired residual light since the operative room lights were on. Data were directly acquired by the laptop via an USB link. A C++ software acquired and processed the images using open source tools such as Qt (v5. 9. 4), openCV (v3. 2. 0), 27 FFTW (v3. 3. 7),  28 and pylon (BASLER library). 8 bits RGB images were acquired every 33 ms (the sampling rate is set to 30 frames per second) with a resolution which at best is 400 ppi (the minimum size of a square pixel is 64 × 64 μm).
Acknowledgments This work was funded by  LABEX PRIMES  (No. ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir " (No. ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR) ;  Cancéropôle Lyon Auvergne Rhône Alpes (CLARA)  within the program "OncoStarter"; and  Infrastructures d'Avenir en Biologie Santé  (No. ANR-11-INBS-000 ), within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR)  and  France Life Imaging. We also want to acknowledge the  PILoT facility  for the support provided on the image acquisition.
Study population The APIPPRA study will recruit study subjects who are at high risk of developing RA and who show no clinical evidence of joint swelling. Participants may be recruited by using a range of ethically approved methods, including during rheumatology out-patient clinics, following referral to recruiting centres from other rheumatology out-patient clinics, referrals from general practitioner (GP) practices through the routine referral pathway or patient identification centre (PIC) pathway, or through existing research databases and registries. Subjects may be identified by pre-screening of laboratory results for RF and ACPA.
Statistical considerations Randomisation procedure, blinding and data management If the participant is willing to participate in the trial, informed consent will be obtained at the start of the screening visit (Additional files 1 and 2). Screening assessment data will be entered by sites onto a web-based InferMed MACRO Electronic Data Capture (EDC) system hosted on a dedicated secure website by King's College London, using only the participant's initials and date of birth as identifiers (Additional file 3). The EDC system will automatically assign a unique participant identification number (PIN) to each participant as they are registered onto the EDC system. Participants who consent to screening but who are subsequently found to be ineligible will also be recorded in the EDC system for CONSORT (Consolidated Standards of Reporting Trials) reporting purposes.
Withdrawal of subjects Participants will be free to withdraw at any time. Participants who do withdraw from IMP will be invited to return for milestone assessments (at months 3, 6, 9 and 12 or at months 15, 18, 21 and 24, depending on the phase of the study) so that data may be collected and changes in their disease can be assessed; it will be made clear to them that this is entirely at their own discretion. Subjects will discontinue investigational product (and non-investigational product at the discretion of the investigator) for any of the following reasons: 1. Withdrawal of informed consent (subject's decision to withdraw for any reason) (Additional files 1 and 2). 2. Any clinical adverse event, laboratory abnormality, or intercurrent illness which, in the opinion of the PI, indicates that continued participation in the study is not in the best interest of the subject.
For each binary outcome, a stratified difference in proportions using the Cochran-Mantel-Haenszel method will be used to compare arms accounting for the randomisation stratifiers in their nominal categorical form. For each continuous outcome, including DAS28, differences in the mean of the outcome between arms will be estimated by using a linear mixed effects regression model of the repeated measures of the outcome across follow-up. 'Visit' will be included in the model as a continuous covariate with full polynomial terms equivalent to including 'Visit' as a categorical factor. Outcome data will be included from the regular visits and from times of measurement, including at ultrasound visits. The model will have a heterogeneous variance first-order autoregressive covariance structure, where measurements at the non-regular visits are included in the variance part of the model at the nearest visit and are included in the mean part of the model in proportion to how the measurement was timed between two visits.
The 'visit' covariate will be located with an origin at 24 months, so that the model with the interactions of the 'time' terms with the other included main effect covariates of study arm, the randomisation stratifiers, and the baseline of the outcome and its missing indicator will directly provide the estimate of the study arm effect and its standard error. The missing indicator method  [21]  will be used to enable those participants having any outcome data, but with missing baseline data, to contribute to the estimate. Descriptive statistics will be reported for measures of acceptability, feasibility and safety. Percentage measures will be reported with exact 95% confidence intervals. There is no plan to have stopping rules. It is anticipated that the data monitoring committee (DMC) will request interim data on safety and will advise on further data required for monitoring and on trial statistician blinding status.
Quality assurance This trial will be monitored to ensure compliance with the trial protocol, GCP and all applicable regulations and to protect scientific integrity. Study management staff will undertake routine quality-control checks of the data. This will include additional central and site-based data checking to ensure that the data quality is accurate. Data queries will be raised, responded to and closed within the EDC system. Range and validation checks will be programmed into the EDC system to minimise transcription errors. Source data verification checks undertaken onsite will be documented to ensure that the final dataset has not been amended after checks have been completed. Checks of randomisation data will be undertaken periodically to identify any errors. Prior to database lock, all SAEs reported via fax or email will be cross-checked with the EDC system to ensure that all are present in the analysis dataset.
We have applied usual regression methods to an in situ data set of particulate organic carbon (POC), total chlorophyll-a (TChla), optical particulate backscattering coefficient (b bp ), and 19 years of monthly TChla and b bp ocean colour data. Results of the regression analysis have been used to calculate phytoplankton carbon biomass (C phyto ) and POC from: i) BGC-Argo float observations; ii) oceanographic cruises, and iii) satellite data. These applications enable highlighting the differences in C phyto and POC estimates relative to the choice of the method. An analysis of the statistical properties of the dataset and a detailed description of the hypothesis of the work drive the selection of the linear regression method.
Differences estimates of the slopes and intercepts obtained by applying different methods depend on the degree of correlation between x and y. When x and y are strongly significantly correlated the differences between the three type-II methods based on the major axis are usually small. Nonetheless, all of them differ from OLS regression to a larger extent  [1]. Yet, when the correlation coefficient tends to 1, the differences between type-II methods and OLS diminish respectively (see Appendix A). In the following sections, we are concentrating only on SMA, as a type-II method, and OLS, as type-I, because both are the most widely used in optical oceanography and field sciences (for more details about their mathematical treatment see Appendix A). Furthermore, MA and RMA cannot be applied to our dataset (i.
Cruise Data The BOUSSOLE project started in 2001, and its activities are developed around a bio-optical buoy located in the deep waters (2440 m) of the Ligurian Sea, one of the sub-basins of the Western Mediterranean Sea [27-29] (http://www. obs-vlfr. fr/Boussole/html/home/home. php). Figure  1  shows the area of study and the location of BOUSSOLE site. The BOUSSOLE site has been visited monthly for buoy servicing, during which 0-400 m casts are performed for the acquisition of conductivity, temperature and pressure (SBE 911plus, SeaBird Inc. , Bellevue, WA, USA). Likewise, water samples are collected at 12 depths (400, 200, 150, 80, 70, 60, 50, 40, 30, 20, 10 and 5 m) with 12 L Niskin bottles mounted on a General Oceanic Rosette equipped with an SBE 32 Carousel Water Sampler, and then subsampled into polycarbonate bottles.
Ocean Colour Data The full ESA OC-CCI v3. 0 (European Space Agency Ocean Colour-Climate Change Initiative version 3. 0) monthly TChla (mg m -3 ) and bbp (m -1 ; 443nm) data time-series at 4 km resolution for the period 1997-2015 over the global ocean was downloaded from the ESA-CCI website (http://www. esa-oceancolour-cci. org/). ESA-CCI products are the results of the merging between SeaWiFS, MERIS, MODIS-Aqua, and VIIRS time-series  [25, [36] [37] [38]. TChla was estimated with a blending of the OCI (as implemented by NASA, itself a combination of CI and OC4), the OC5 (NASA, 2010) and the OC3 algorithms (http://www. esa-oceancolour-cci. org/?q=webfm_send/684). The Quasi-Analytical Algorithm (QAA) was used to compute bbp  [39, 40]. The accuracy of the QAA algorithm was demonstrated in several recent studies  [12, 13, 19, [23] [24] [25] 41].