Electronic nutritional intake assessment in patients with urolithiasis: A decision impact analysis

Purpose: To evaluate a physician’s impression of a urinary stone patient’s dietary intake and whether it was dependent on the medium through which the nutritional data were obtained. Furthermore, we sought to determine if using an electronic food frequency questionnaire (FFQ) impacted dietary recommendations for these patients. Materials and Methods: Seventy-six patients attended the Stone Clinic over a period of 6 weeks. Seventy-five gave consent for enrollment in our study. Patients completed an office-based interview with a fellowship-trained endourologist, and a FFQ administered on an iPad. The FFQ assessed intake of various dietary components related to stone development, such as oxalate and calcium. The urologists were blinded to the identity of patients’ FFQ results. Based on the office-based interview and the FFQ results, the urologists provided separate assessments of the impact of nutrition and hydration on the patient’s stone disease (nutrition impact score and hydration impact score, respectively) and treatment recommendations. Multivariate logistic regressions were used to compare pre-FFQ data to post-FFQ data. Results: Higher FFQ scores for sodium (odds ratio [OR], 1.02; p=0.02) and fluids (OR, 1.03, p=0.04) were associated with a higher nutritional impact score. None of the FFQ parameters impacted hydration impact score. A higher FFQ score for oxalate (OR, 1.07; p=0.02) was associated with the addition of at least one treatment recommendation. Conclusions: Information derived from a FFQ can yield a significant impact on a physician’s assessment of stone risks and decision for management of stone disease

International Network of Networks Data Experts Workshop_A RDA Guide to International Data Research

Award Abstract #1848888 International Network-of-Networks (iN2N): Data Experts Worksho

RDA CURE-FAIR WG

A Research Data Alliance (RDA) Working Grou

Repeat: A Framework to Assess Empirical Reproducibility in Biomedical Research

RepeAT includes 103 unique variables grouped into five categories (research design and aim, database and data collection methods, data mining and data cleaning, data analysis, data sharing and documentation). Preliminary results in manually processing 40 scientific manuscripts indicate components of the proposed framework with strong inter-rater reliability, as well as directions for further research and refinement of RepeAT

Repeat: A Framework to Assess Empirical Reproducibility in Biomedical Research

RepeAT includes 103 unique variables grouped into five categories (research design and aim, database and data collection methods, data mining and data cleaning, data analysis, data sharing and documentation). Preliminary results in manually processing 40 scientific manuscripts indicate components of the proposed framework with strong inter-rater reliability, as well as directions for further research and refinement of RepeAT

FAIRness for FHIR: Towards Making Health Datasets FAIR Using HL7 FHIR.

Medical data science aims to facilitate knowledge discovery assisting in data, algorithms, and results analysis. The FAIR principles aim to guide scientific data management and stewardship, and are relevant to all digital health ecosystem stakeholders. The FAIR4Health project aims to facilitate and encourage the health research community to reuse datasets derived from publicly funded research initiatives using the FAIR principles. The 'FAIRness for FHIR' project aims to provide guidance on how HL7 FHIR could be utilized as a common data model to support the health datasets FAIRification process. This first expected result is an HL7 FHIR Implementation Guide (IG) called FHIR4FAIR, covering how FHIR can be used to cover FAIRification in different scenarios. This IG aims to provide practical underpinnings for the FAIR4Health FAIRification workflow as a domain-specific extension of the GoFAIR process, while simplifying curation, advancing interoperability, and providing insights into a roadmap for health datasets FAIR certification

Repeat: a framework to assess empirical reproducibility in biomedical research

Abstract Background The reproducibility of research is essential to rigorous science, yet significant concerns of the reliability and verifiability of biomedical research have been recently highlighted. Ongoing efforts across several domains of science and policy are working to clarify the fundamental characteristics of reproducibility and to enhance the transparency and accessibility of research. Methods The aim of the proceeding work is to develop an assessment tool operationalizing key concepts of research transparency in the biomedical domain, specifically for secondary biomedical data research using electronic health record data. The tool (RepeAT) was developed through a multi-phase process that involved coding and extracting recommendations and practices for improving reproducibility from publications and reports across the biomedical and statistical sciences, field testing the instrument, and refining variables. Results RepeAT includes 119 unique variables grouped into five categories (research design and aim, database and data collection methods, data mining and data cleaning, data analysis, data sharing and documentation). Preliminary results in manually processing 40 scientific manuscripts indicate components of the proposed framework with strong inter-rater reliability, as well as directions for further research and refinement of RepeAT. Conclusions The use of RepeAT may allow the biomedical community to have a better understanding of the current practices of research transparency and accessibility among principal investigators. Common adoption of RepeAT may improve reporting of research practices and the availability of research outputs. Additionally, use of RepeAT will facilitate comparisons of research transparency and accessibility across domains and institutions