Suchergebnisse

Objective: Giving information and providing advice on diagnostic tests is one of the tasks physicians must carry out personally. To do so, they must evaluate the evidence and integrate their findings into everyday practice. Clinical decisions should be based on evidence. How well current medical education prepares for such evidence-based clinical decision-making is largely unclear. Therefore, it was examined how confident medical students are in clinical decision-making based on evidence using epidemiological data. It was examined whether the decision-making confidence increases the higher the semester. Further questions were whether scientifically active medical students show higher decision-making confidence and whether the representation of figures as pictograms rather than tables positively influences the decision-making confidence. Methods: An online survey of the medical students of the Friedrich-Alexander-University Erlangen-Nürnberg was carried out. Respondents were presented with three clinical decision-making situations in random order for evaluation in the form of screening scenarios. In each case, the decision-making confidence also had to be specified. The scenarios contained only epidemiological data on existing screening tests. For each scenario, the numbers were presented as a table or a pictogram in a random fashion. In order to avoid false confidence resulting from preconceived opinions neither the illnesses nor the screening tests were mentioned by name. Results: Answers from 171 students were evaluated. Decision-making confidence in dealing with the numbers does not increase in higher semesters (r(Pearson)=0.018, p=0.41). Scientific work is not associated with a higher decision-making confidence (t(169)=-1.26, p=0.11, d=-0.19). Presentation as a pictogram leads to a higher decision-making confidence compared to tables (Pictogram: M=2.33, SD=1.07, Table with numbers: M=2.64, SD=1.11, t(511)=3.21, p<0.01, d=0.28). Conclusions: Medical students from higher semesters show no higher ...

One year ago, the novel COVID-19 infectious disease emerged and spread, causing high mortality and morbidity rates worldwide. In the OBO Foundry, there are more than one hundred ontologies to share and analyse large-scale datasets for biological and biomedical sciences. However, this pandemic revealed that we lack tools for an efficient and timely exchange of this epidemiological data which is necessary to assess the impact of disease outbreaks, the efficacy of mitigating interventions and to provide a rapid response. Recently, several new COVID-19 ontologies have developed such as the IDO extension or CIDO. Hence, our research question was to determine if there was a good representation of epidemiological quantitative concepts in OBO ontologies. Our objectives were to identify missing COVID-19 epidemiological terms and implement axiom patterns for extensions to existing ontologies or to build a new, logically well-formed, and accurate ontology in OBO. In this study we present our findings and contributions for the bio-ontologies community. ; This initiative has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N°825575 (the European Joint Programme Rare Diseases), and the ZonMW Health Holland under the Trusted World of Corona (TWOC) project number LSHM20070.

Background: Although demands on family physicians (FP) are to a large extent similar in the European Union, uniform assessment standards for family medicine (FM) specialty training and assessment do not exist. Aim of this pilot study was to elicit and compare the different modalities and assessment methods of FM specialty training in five European countries.

The novel COVID-19 infectious disease emerged and spread, causing high mortality and morbidity rates worldwide. In the OBO Foundry, there are more than one hundred ontologies to share and analyse large-scale datasets for biological and biomedical sciences. However, this pandemic revealed that we lack tools for an efficient and timely exchange of this epidemiological data which is necessary to assess the impact of disease outbreaks, the efficacy of mitigating interventions and to provide a rapid response. In this study we present our findings and contributions for the bio-ontologies community. ; We would like to specially thank Birgit Meldal for her input and ideas. This initiative has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N°825575 (the European Joint Programme Rare Diseases), and the Trusted World of Corona (TWOC; LSH Health Holland).

Rett syndrome (RTT) is a rare neurological disorder mostly caused by a genetic variation in MECP2. Making new MECP2 variants and the related phenotypes available provides data for better understanding of disease mechanisms and faster identification of variants for diagnosis. This is, however, currently hampered by the lack of interoperability between genotype-phenotype databases. Here, we demonstrate on the example of MECP2 in RTT that by making the genotype-phenotype data more Findable, Accessible, Interoperable, and Reusable (FAIR), we can facilitate prioritization and analysis of variants. In total, 10,968 MECP2 variants were successfully integrated. Among these variants 863 unique confirmed RTT causing and 209 unique confirmed benign variants were found. This dataset was used for comparison of pathogenicity predicting tools, protein consequences, and identification of ambiguous variants. Prediction tools generally recognised the RTT causing and benign variants, however, there was a broad range of overlap Nineteen variants were identified that were annotated as both disease-causing and benign, suggesting that there are additional factors in these cases contributing to disease development. ; The authors would like to thank the Mutalyzer team for support and feedback, Henk van Kranen for support in liftover of ancient genetic variant descriptions, and Eric Smeets for collection of the Maastricht Rett dataset.This work was funded by ELIXIR (funded by the European Commission within the Research Infrastructures programme of Horizon 2020), the research infrastructure for life-science data (MolData2). FE and LC were also funded by The Dutch Rett Syndrome Foundation (Stichting Terre). CE, AJ, RK, AV, SCG, MB, MRi and MR also received funding from EXCELERATE (H2020, Grant No. 676559). AJ, RK, MR, MB, and SCG also received funding from RD-Connect, European Union Seventh Framework Programme (FP7/2007–2013, Grant No. 305444). FE, CE, AJ, RK, MR, MB, and SCG received funding from the European Union's Horizon 2020 Research and Innovation Program under grant agreement EJP RD N°825575. RK was also funded by NWO in project VWData (grant no. 400.17.605) and BBMRI-NL (NWO, National Roadmap for Large-Scale Research Facilities, grant no. 184.033.111). AV and SCG also received funding from INB Grant (Grant No. PT17/0009/0001 - ISCIII-SGEFI / ERDF). ; Peer Reviewed ; Postprint (published version)

In rare disease (RD) research, there is a huge need to systematically collect biomaterials, phenotypic, and genomic data in a standardized way and to make them findable, accessible, interoperable and reusable (FAIR). RD-Connect is a 6 years global infrastructure project initiated in November 2012 that links genomic data with patient registries, biobanks, and clinical bioinformatics tools to create a central research resource for RDs. Here, we present RD-Connect Registry & Biobank Finder, a tool that helps RD researchers to find RD biobanks and registries and provide information on the availability and accessibility of content in each database. The finder concentrates information that is currently sparse on different repositories (inventories, websites, scientific journals, technical reports, etc.), including aggregated data and metadata from participating databases. Aggregated data provided by the finder, if appropriately checked, can be used by researchers who are trying to estimate the prevalence of a RD, to organize a clinical trial on a RD, or to estimate the volume of patients seen by different clinical centers. The finder is also a portal to other RD-Connect tools, providing a link to the RD-Connect Sample Catalogue, a large inventory of RD biological samples available in participating biobanks for RD research. There are several kinds of users and potential uses for the RD-Connect Registry & Biobank Finder, including researchers collaborating with academia and the industry, dealing with the questions of basic, translational, and/or clinical research. As of November 2017, the finder is populated with aggregated data for 222 registries and 21 biobanks. ; This work has been supported by the European Union Seventh Framework Programme (FP7/20072013) under grant agreements no. 305444 (RD-Connect). RD-Connect has a main role in funding authors contributing to the study design, data collection and analysis, decision to publish, or preparation of the manuscrip. NeurOmics (no. 305121, http://rd-neuromics.eu/) and EURenOmics (no. 305608, http://www.eurenomics.eu/) have had mainly a role of data providers, since several registries participating to the Registry & Biobank Finder collaborate with the two projects. We thank ODEX4all (NWO 650.002.002), ELIXIR funded through participating member states, and ELIXIR-EXCELERATE funded through the European Commission within the Research Infrastructures Programme of Horizon 2020 (grant agreement number 676559) for collaborating to the study design and software development especially with a view to interoperability with other systems (MR, MT). MF and MM are funded by Fondazione Telethon (Project GTB12001; Telethon Network of Genetic Biobanks). ; Sí

Rare diseases (RD) patient registries are powerful instruments that help develop clinical research, facilitate the planning of appropriate clinical trials, improve patient care, and support healthcare management. They constitute a key information system that supports the activities of European Reference Networks (ERNs) on rare diseases. A rapid proliferation of RD registries has occurred during the last years and there is a need to develop guidance for the minimum requirements, recommendations and standards necessary to maintain a high-quality registry. In response to these heterogeneities, in the framework of RD-Connect, a European platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research, we report on a list of recommendations, developed by a group of experts, including members of patient organizations, to be used as a framework for improving the quality of RD registries. This list includes aspects of governance, Findable, Accessible, Interoperable and Reusable (FAIR) data and information, infrastructure, documentation, training, and quality audit. The list is intended to be used by established as well as new RD registries. Further work includes the development of a toolkit to enable continuous assessment and improvement of their organizational and data quality. ; Supported by the RD-CONNECT: an integrated platform connecting registries, biobanks and clinical bioinformatics for rare disease research, which received funding from the European Union within the framework of FP7 Collaborative projectHEALTH.2012.2.1.1-1-C [Grant agreement number: 305444]. Supported partly also by EuRRECa: European Registries for Rare Endocrine Conditions, which received funding from the European Union within the framework of CHAFEA Health Programme (2014–2020) [Grant agreement number: 777215] and the COST Action CA16210 "Maximising Impact of research in NeuroDevelopmental Disorders". ; Sí