dataesr/scientific-paragraphs-categorization huggingface dataset:
Safety As the study is low-risk, a formal Data Monitoring Committee is not necessary. Instead, the EARB will review on a regular basis (monthly initially) AEs and SAEs and their relatedness with study intervention. Any decision to stop the trial will be made in conjunction with the TSC. There is a small risk of falls when introducing new glasses. Therefore, the optometrist or vision specialist will introduce the glasses step-wise where necessary. Consistent and thorough checking by the SST will occur to ensure that visual devices are appropriate and, where inappropriate, will refer back to clinical services to refine the prescription. The SI up to 18 weeks may be a large commitment for some participants, so we will make clear the benefit to them and be flexible around participant availability for the SI visits.
Data management of the RCT Different tasks of data management (from study design to database closure) and the responsibilities of each person involved in the data management process and quality control are detailed in a data management plan. Data are collected by using an eCRF (screening, baseline and follow-up data); diaries, completed by PwD and their companion (to assess whether the intervention is acceptable, tolerated, helpful and useful and to include any comments relevant to the intervention components or delivery); a logbook (SI data), completed by the SST; and qualitative interviews, audio-recorded. Any original document or information recorded during the study is defined as a source document and the eCRF must accurately reflect the data in the source document. Source document in the framework of the study can be paper CRF, original copy of scales or medical files.
Consistency checks will be programmed by the data manager to check the consistency and the completion of data in the eCRF. The list of consistency checks will be predefined by the project team and passed on to the data manager who will write a study-specific data validation plan. Additional queries might also be raised by the clinical research assistant. Queries are sent to the clinical site via the eCRF. The data manager will complete self-evident corrections (SECs) in the database following rules defined in the SEC plan validated and signed by the sponsors and the investigators before implementation. Remote and onsite monitoring is organised throughout the trial to ensure compliance to the protocol, regulations and GCP recommendations.
Interim analyses and stopping guidelines Since this is a very-low-risk RCT, there is no data monitoring and ethics committee (DMEC) and no interim analysis is planned for either safety or futility analysis. Public access to the full protocol, participant-level dataset, and statistical code The full protocol will be communicated with primary publication of study results (and statistical code depending on the journal), and participant-level dataset (and statistical code) will be accessible through request to the EARB.
Acknowledgements The authors thank the EUCLID/F-CRIN clinical trials platform for overall development and assistance of the RCT:  Monique Termote  and  Charly Matard  (data managers),  Nathalie Chaghil-Boissiere  (coordinator clinical research associate),  Christine Schwimmer  (EUCLID executive director). The authors thank  Lucas Wolski  at the  University of Freiburg  for assisting design of the qualitative interview. We thank industry collaborators at the  Outside Clinic, Starkey Hearing Technologies, Essilor International, Siemens Hearing Aids, Sivantos Group, Peek Vision and HearCheck. We thank  Joshua Woods  for input in the development of the sensory intervention and  Brenda Gannon  for health economics input. We thank the  Greater Manchester NIHR Clinical Research Network  for supporting the study.
A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers.
Introduction Within the 2019 International Medical Informatics Association (IMIA) Yearbook, the goal of the Bioinformatics and Translational Informatics (BTI) section is to provide an overview of research trends from 2018 publications that demonstrated excellent research about various aspects of bioinformatics methods and techniques to advance clinical care. In 2008, the American Medical Informatics Association (AMIA) has defined translational bioinformatics as "… the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health"  [1]. First priorities addressed storage and retrieval, and focused analytics of high-throughput data motivating numerous research and development studies in the last decade.
Paper Selection Method Following the method described in  [2] , a comprehensive review of articles published in 2018 and addressing various subtopics for BTI was conducted. The selection was performed by querying MEDLINE via PubMed (from NCBI, National Center for Biotechnology Information) with a set of predefined Medical Subject Headings (MeSH) descriptors along with free terms: Bibliographic databases were searched on February 24 th , 2019 for papers published in 2018, considering the electronic publication date. The original set of 636 references was reviewed jointly by the two section editors to select a consensual list. Hence, 42 (respectively 45) references were selected by the first (second) section editor based on the title and abstract of papers. Among the 16 papers in common, three were excluded due to moderate interest and critical length.
In the recent year, the most spectacular results have been obtained for images analysis (and especially for photographic image analysis, e. g. , digital pathology images, retina pictures. ), but numerous other fields are investigated. Trend 2: Pan-cancer approach and integration of multi-omics data for more insightful analyses The large availability of high throughput data in the context of biological and clinical research but also during normal patient care has enabled the development of new approaches to classify diseases and to identify potentially better or new treatments for known diseases. The best paper candidates highlight this trend in four contributions using multi-omics datasets (leveraging gene expression, methylation, proteomics, next generation sequencing (NGS),…etc. ). These new approaches are generating a great amount of interest and a large body of work as represented by the selection of three of the four best papers of the BTI section.
In their work Sengupta et al. , adopted a pan-cancer approach to take benefit of multi-omics data for drug repurposing  [11]. Their goal is to identify drugs approved by the Food and Drug Administration (FDA) for cancer location not yet mentioned in the approval. The authors rely on the Database of Evidence for Precision Oncology (DEPO), a tool they built and presented in  [18] , to link druggability to genomic, transcriptomic, and proteomic biomarkers. They used a pan-cancer cohort of more than 6,500 tumors to identify those with potential druggable markers. The authors rely on the DEPO database (integrating genomic, transcriptomic, and proteomic data, as well as clinical data over several types of cancer samples) and structural alignment tools for identifying tumors with potentially druggable biomarkers consisting of drug-associated mutations, micro-RNA expression outliers, and protein/ phosphoprotein expression outliers.
Emerging Topic 1: Towards clinician-friendly infrastructures for data integration and analyses The growing complexity of biological and clinical datasets requires new tools to help researchers in data management and exploration process. This year saw a number of contributions providing experts with innovative ways to interact with multi-omics and clinical datasets. Among the 13 candidate best papers, two were addressing the issue of data exploration  [9, 4] , and one described a large national infrastructure hosting data and samples at a national level  [7]. Moscatelli et al. , present an infrastructure design to simplify the exploration of clinical data, both structured (e. g. , laboratory data) and unstructured (free text clinical reports)  [9]. Their infrastructure relies on a NoSQL structure, and manages anonymization and machine learning layers to assist the research in the mining of data.
Krempel et al. , introduce the CancerSysDB, a web-based application designed to host multi-omics pan-cancer data, and to simplify the querying process  [4]. CancerSysDB is open-source and can host both public datasets, and especially data coming from the TCGA, but also private datasets through the use of a self-hosted instance of the system. Workflows can be connected to the application and dynamically uploaded to the application.
Conclusion and Outlook A few interesting papers published in 2018 in the BTI scope matched the IMIA Yearbook special topic "Artificial Intelligence". These papers illustrate well the complexity and the constraints induced by the deployment of deep-learning techniques, especially in the context of multidisciplinary and personalized care (including molecular characterization of tumors. ). Further intelligent approaches are expected in coming years, combining semantic web languages with clinical omics data and biomolecular knowledge for extracting self-explanatory and actionable knowledge nuggets in clinical settings. It is worth noting that many contributions keep on relying on public datasets (such TCGA…), as well as open tools and systems. In this context, the emergence of clinician-friendly exploration and analysis platforms should bring closer the clinical, translational, and bioinformatics communities.
Study procedures and follow-up Patients will be followed until 6 months after trauma or until death, whichever comes first. In addition, at the 6month follow-up contact, the researcher or research assistant will document any secondary intervention that may be planned for the patient. A schedule of events is shown in Table  1. A flow chart is shown in Fig. 1. Following enrolment, treatment (either nonoperative or operative management) will be decided using a shared decision approach as mentioned above. Data will be acquired at 7, 14, and 30 days, and at 3 and 6 months after trauma. Baseline data will be collected from the patient's medical files or by interviewing the patient as soon as possible, but no later than at the visit after enrolment. At each follow-up visit, the coordinating researcher or research assistant will visit the patient.
Statistical analysis Data will be analyzed using the Statistical Package for the Social Sciences (SPSS) version 24. 0 or higher (SPSS, Chicago, Ill. , USA) and will be reported following the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines. Normality of continuous data will be tested with the Shapiro-Wilk test. Homogeneity of variances will be tested using the Levene's test. A two-sided p-value < 0. 05 will be taken as threshold of statistical significance in all statistical tests. The analysis will be performed on a per-protocol basis, (as that is the most conservative approach) and verified by an intention-to-treat analysis if needed. Patients who have died will remain in the analysis as censored. If necessary, missing values will be replaced using multiple imputation following the predictive mean matching method, using ten imputations.
Descriptive analysis will be performed in order to report the outcome measures and other variables collected for the entire population as well as per treatment group. Continuous data will be reported as mean and SD (if Normal) or median and quartiles (if non-Normal), categorical data as number with percentage. Univariate comparison between the groups will be done using Student's T or Mann-Whitney U-test (for Normal and non-Normal data, respectively), or Chisquared test or Fisher's Exact test (categorical data, as applicable). The Student's T-test will be done with equal variance assumed or not assumed, based upon the results of the Levene's test. For the primary outcome measure, a non-inferiority margin of 0. 15 points for mean EQ-5D scores was chosen because it is reasonable and on the border of what would be considered a clinically important effect.
The economic evaluation will be performed in accordance with the intention-to-treat principle. Missing data will be imputed using multiple imputation by changed equations  [46]. The Incremental Cost-Effectiveness Ratio (ICER) of nonoperative versus operative management will be calculated by dividing the difference in costs by difference in effects. A cost-utility analysis, with QALY (based on the EQ-5D utility score) as outcome measure, will be done as primary economic analysis. As secondary analysis, a cost-effectiveness analysis, with QUALIDEM as outcome measure, will be done. In order to account for the possible clustering of data, analyses will be performed using linear multilevel analyses  [47]. Accounting for the possible clustering of data (e. g. , at the hospital level) is very important, as most economic evaluations fail to do so, whereas ignoring the possible clustering of data might lead to inaccurate levels of uncertainty and inaccurate point estimates  [47].
A randomized controlled trial (RCT) would have avoided selection bias, however randomizing patients with a limited life expectancy was considered unfeasible (and unethical) by the research team. Patient recruitment in trials randomizing between operative and nonoperative treatment is known to be challenging, and would likely introduce an unacceptably high level of selection bias; surgeons with clear preference for a particular treatment may be unwilling to inform patients about the study, and patients (or their proxy if patient is unable to decide) with preference for a particular treatment will not sign informed consent. The chosen observational design allows all centers and all patients to participate, and real-life data will be collected. For patients with severe cognitive impairment, informed consent will be obtained by their health care proxy.
Acknowledgements We would like to thank the following societies for supporting the project:  Society for surgery (Dutch Association of Surgery (NVvH ),  Dutch Association of Trauma Surgery (NVT) ,  Dutch Orthopaedic Association (NOV) , and  Dutch Association of Orthopaedic Surgery (NVOT)), clinical geriatrics (Dutch Association of Clinical Geriatrics NVKG)), elderly care (Association of Elderly Care Physicians (VERENSO) ) and the  Dutch Patient Federation. FRAIL-HIP study group  Lisanne Balemans ,  MD 11  ,  Frank W. Bloemers MD PhD 12  ,  Janneke Bos ,  MD 13  ,  Jantien Brouwer, MD PhD 14  ,  Bart J. Burger, MD PhD 15  ,  Judella O. Daal , MD 16,17 ,  Annemarieke De Jonghe , MD PhD 18 ,  Matthea Dijkshoorn, MD 19  ,  Michael J. R. Edwards, MD PhD 20  ,  Ellen A. Elbrecht ,  MD 21  ,  Miriam C.
The selection and quantification of the indicators ideally relies on data which are regularly updated, cover a relevant spatial scale for wetlands and which are available for the whole Mediterranean basin. For most indicators this is not (yet) feasible and a whole range of methods are applied to obtain quantifications and estimates of the trends, depending on data availability. An explication on the data sources and methodological approach is provided for each indicator in each MWO, with methods including for instance remote sensing, upscaling from a network of in-situ observations or sample sites, downscaling from global and national datasets, modeling, literature reviews and more.
Prompt photons are identified in the ATLAS experiment by means of selections on quantities describing the shape and properties of the associated electromagnetic showers, and by requiring them to be isolated from other particles in the event. These selections are separately optimised for those photon candidates that convert into an electron-positron pair before reaching the electromagnetic calorimeter (converted photons), and those photon candidates that are not associated with a conversion (unconverted photons). As already observed using LHC data in Run 1  [7] , the efficiency of the selection criteria is modelled by Monte Carlo (MC) simulation with only O(2-5%) accuracy, being mostly limited by an imperfect description of the electromagnetic shower development in the detector. The photon identification efficiency can on the other hand be measured in data with a precision of O(1%).
Photon reconstruction The interactions of photons and electrons with the ATLAS EMC produce similar electromagnetic showers, depositing a significant amount of energy in a restricted number of neighbouring calorimeter cells. As photons and electrons have very similar signatures in the EMC, their reconstruction proceeds in parallel. The reconstruction of electron candidates, including a dedicated, cluster-seeded track-finding algorithm to increase the efficiency for the reconstruction of low-momentum electron tracks, is described in Ref. [11]. The reconstruction of unconverted and converted photons in Run 2 data collected in 2015 and 2016 is largely unchanged from the reconstruction used Run 1 and described in Ref. [7] , and can be summarised as follows: • A sliding window with a size of 3 × 5 in units of ∆η × ∆φ = 0.
Collision and simulated data samples The measurements presented in this paper use proton-proton (pp) collisions at √ s = 13 TeV recorded by the ATLAS detector in 2015 and 2016 during the LHC Run 2. The data are required to pass good quality requirements on the detector performance and object reconstruction, leading to 36. 1 fb -1 of integrated luminosity. The inclusive photon measurement discussed in Section 5. 3 relaxes the requirement on the performance of the ATLAS muon spectrometer, and uses 36. 7 fb -1 of integrated luminosity. In these datasets the mean number of interactions per bunch crossing is 13. 5 in 2015 data and 24. 9 in 2016 data. Two of the methods used to measure the photon identification efficiency described in Section 5 rely on the use of Z boson decays into electron-positron pairs Z → e + e -and on Z boson radiative decays Z → γ ( = e, µ): these events are selected in data collected with the lowest-threshold unprescaled lepton triggers.
Data-driven corrections to shower shapes in simulated data The distributions of the photon transverse shower shapes in the ATLAS MC simulation do not perfectly describe those observed in data. While these distributions in simulation are rather similar in shape to those found in the data, small systematic differences in their average values are observed, pointing to a mismodelling in MC simulation of the lateral profile development of the electromagnetic showers, while, overall, the longitudinal electromagnetic shower profiles are well described. These differences between data and MC distributions are measured and parameterised as simple shifts to be applied to the MC-simulated values to align with the distributions observed in data. The shifts are calculated by minimizing the χ 2 between the data and the shifted MC distributions of photon candidates satisfying the tight identification criteria and the calorimeter isolation requirement described in the previous section.
Inclusive photon method This method is based on an inclusive photon sample collected by single-photon triggers. These triggers have thresholds ranging from 10 GeV to 140 GeV and require loose photon identification selections. They are prescaled except at the 140 GeV threshold, but provide large photon datasets at high E T , with a few hundred events in each bin for E T > 250 GeV, allowing efficiency measurements to be performed up to E T ∼ 1. 5 TeV. In addition to the trigger requirements, the photons are required to have E T > 25 GeV and |η| < 2. 37, excluding the region 1. 37 ≤ |η| < 1. 52, and to pass the loose isolation requirements described in Section 3. 3. However, the purity of the sample, defined as the fraction of true photon candidates, is low, especially at low photon E T , both with and without the identification cuts applied.
The uncertainty reaches 18% at low E T but is about 3% at 50 GeV and below 1% at high E T. • An uncertainty due to the tight isolation requirement is evaluated by changing the size of the isolation cone from 0. 4 to 0. 2. The uncertainty reaches 8% at low E T , but is less than 1% above 50 GeV. • An uncertainty due to the description of the detector in simulated samples is derived in the same way as for the method using radiative Z decays, using samples with variations in the amount of inactive material in front of the calorimeter. The size of the uncertainty is typically 1% at low E T and at the per-mil level at high E T , except for the unconverted photons in the first endcap bin where uncertainties reach 4%. • The statistical uncertainties in the simulation samples are accounted for using the electron extrapolation method by iteratively resampling the corresponding datasets, and are typically 0.
5% or less. The statistical uncertainty is computed as the width of the distribution of results obtained when repeating the measurement on pseudo-datasets obtained by resampling the data and reach 1-2% for E T < 50 GeV and typically 0. 5% at higher E T. The total uncertainty reaches to 7-18% at E T = 25 GeV, but 2-3% at 40 GeV and 1% or less above 100 GeV except for unconverted photons for E T > 250 GeV and 1. 52 ≤ |η| < 1. 81 where it reaches 4% as noted above. Results are shown in Figures  7  and 8.
The combination is performed using the BLUE algorithm  [39, 40] : the combined scale factor in each measurement bin computed as a linear combination of the per-method scale factors, with coefficients that are obtained from the values of their statistical and systematic uncertainties and their correlations. Statistical uncertainties are taken to be uncorrelated since the methods are applied to disjoint datasets. Systematic uncertainties in material modelling, which affect both the radiative Z and inclusive photon method, are expected to have correlated effects in these two measurements. However, since these methods provide measurements in largely disjoint photon E T intervals, the correlation assumption has a negligible impact on the combined result. All sources of statistical and systematic uncertainties are therefore assumed to be uncorrelated.
Conclusion The reconstruction of photon candidates measured in the ATLAS detector is improved in several ways for the 2015 and 2016 Run 2 data-taking periods at the LHC with pp collisions at √ s = 13 TeV, building on what was implemented for Run 1. A more robust reconstruction of photon conversions is introduced to cope with the higher pile-up conditions. The photon identification criteria, based on rectangular cuts on calorimetric variables, are also tuned to reduce the dependence of the identification efficiency on pile-up. The efficiency of the tight identification criteria is measured from from 36. 1 fb -1 to 36. 7 fb -1 of pp collision data for isolated photon candidates, using three independent methods in different photon E T ranges. The three measurements agree within their uncertainties in the overlapping E T ranges, and are combined.