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The Horizon 2020 project EOSC-Life brings together the 13 Life Science ‘ESFRI’ research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that purpose, a toolbox is developed, providing information to researchers who wish to share or use sensitive data. The authors developed a categorisation system designed to support consistent labelling and categorisation of the resources stored in the toolboxin terms most relevant to data sharing tasks and activities, so that they are available to users (e.g., as on screen filters) when searching the system. The poster describes the methods and main results of a pilot study set to evaluate the proposed categorisation system for the toolbox resources with respect to applicability, reliability, sustainability and consistency. The primary objective of the pilot study was to identify the main improvements that are necessary to arrive at an inter-community, well understood and commonly approved categorisation system.

Simple Summary We compared the predictive and prognostic performance of different lymph node classification systems regarding overall survival in patients with colorectal cancer (CRC). Distinct lymph node ratio (LNR) and Log odds of positive lymph nodes (LODDS) classifications demonstrated prognostic superiority over the N category only in patients with Stage III CRC. Abstract Background: Lymph node ratio (LNR) and the Log odds of positive lymph nodes (LODDS) have been proposed as a new prognostic indicator in surgical oncology. Various studies have shown a superior discriminating power of LODDS over LNR and lymph node category (N) in diverse cancer entities, when examined as a continuous variable. However, for each of the classification systems various cut-off values have been defined, with the question of the most appropriate for patients with CRC still remaining open. The present study aimed to compare the predictive impact of different lymph node classification systems and to define the best cut-off values regarding accurate evaluation of overall survival in patients with resectable, non-metastatic colorectal cancer (CRC). Methods: CRC patients who underwent surgical resection from 1996 to 2018 were extracted from our medical data base. Cox proportional hazards regression models and C-statistics were performed to assess the discriminative power of 25 LNR and 26 LODDS classifications. Regression models were adjusted for age, sex, extent of the tumor, differentiation, tumor size and localization. Results: Our study group consisted of 654 consecutive patients with non-metastatic CRC. C-statistic revealed 2 LNR and 5 LODDS classifications that demonstrated superior prognostic performance in patients with UICC III CRC, compared to the N category. No clear advantage of one classification over another could be demonstrated in any other patient subgroup. Conclusions: Distinct LNR and LODDS classifications demonstrate a prognostic superiority over the N category only in patients with Stage III radically resected CRC.

Background: A simulation can be a tool to explore methodological challenges of used study designs or data analysing methods. Here, we provide an R-package for sampling multidimensional distributed data within cluster randomized trails, for stepped wedge design (SWD) trails as well as cross-over and[for full text, please go to the a.m. URL], 16. Deutscher Kongress für Versorgungsforschung (DKVF)

This is the Information Age. We can expect that for a particular research question that is empirically testable, we should have a collection of evidence which indicates the best way to proceed. Unfortunately, this is not the case in several areas of empirical research and decision making. Instead, when researchers and policy makers ask a specific question, such as “What is the effectiveness of a new treatment?”, the structure of the evidence available to answer this question may be complex and fragmented (e.g. published experiments may have different grades of quality, observational data, subjective judgments, etc.).

This paper investigates physiological responses to perceptions of unfair pay. We use an integrated approach exploiting complementarities between controlled lab and representative field data. In a simple principal-agent experiment agents produce revenue by working on a tedious task. Principals decide how this revenue is allocated between themselves and their agents. Throughout the experiment we record agents' heart rate variability, which is an indicator of stress-related impaired cardiac autonomic control and has been shown to predict coronary heart diseases in the long-run. Using three measures of perceived unfairness our findings establish a link between unfair payment and heart rate variability. Building on these findings, we further test for potential adverse health effects of unfair pay using data from a large representative data set. The analysis includes cross-sectional and dynamic panel estimations. Complementary to our experimental findings we find a strong and highly significant negative association between health outcomes, in particular cardiovascular health, and the perception of unfair pay.

This paper investigates physiological responses to perceptions of unfair pay. In a simple principal agent experiment agents produce revenue by working on a tedious task. Principals decide how this revenue is allocated between themselves and their agents. In this environment unfairness can arise if an agent's reward expectation is not met. Throughout the experiment we record agents' heart rate variability. Our findings provide evidence of a link between perceived unfairness and heart rate variability. The latter is an indicator of stress-related impaired cardiac autonomic control, which has been shown to predict coronary heart diseases in the long run. Establishing a causal link between unfair pay and heart rate variability therefore uncovers a mechanism of how perceptions of unfairness can adversely affect cardiovascular health. We further test potential adverse health effects of unfair pay using data from a large representative data set. Complementary to our experimental findings we find a strong and highly significant association between health outcomes, in particular cardiovascular health, and fairness of pay.

Einleitung: Vom Straßenverkehr ausgehender Lärm und Abgase sind Risikofaktoren für kardiovaskuläre Erkrankungen. In diesem Zusammenhang wurde postuliert, dass Bewohner sozial benachteiligter Stadtviertel nicht nur überdurchschnittlich häufig durch Straßenverkehr belastet[for full text, please go to the a.m. URL], 54. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)