Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have been advanced. Implementation of these frameworks require multi-modal data and a shift towards open science and principles such as requiring data to be FAIR (Findable, Accessible, Interoperable and Reusable). Advanced analytics such as artificial intelligence require data to be interoperable so data can be exchanged among different technology systems and software applications. The TBI field has multiple ongoing initiatives to promote sharing and data reuse for both pre-clinical and clinical studies, which is an opportunity for the SCI field given these injuries can often occur concomitantly. The adoption of interoperable standards, data sharing, open science, and the use of advanced analytics in SCI is needed to facilitate translation in research and care. It is critical that people with lived experience are engaged to ensure data are relevant and enhances quality of life.
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http://dx.doi.org/10.1016/j.expneurol.2024.115048 | DOI Listing |
Genet Med
December 2024
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN; Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN. Electronic address:
Purpose: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) with genetic data to understand which decisions may affect performance.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
Faculty of Health Sciences, Department of Nursing, Division of Public Health Nursing, Bandırma Onyedi Eylül University, Balıkesir, Turkey.
Aim: This study aimed to translate the Environmental Health Literacy Scale (EHLS) into Turkish and assess its construct validity and internal consistency.
Methods: This research employs a methodological design. The research was conducted during the 2022-2023 academic year with a sample of 500 students from the Faculty of Health Sciences.
Med Care
November 2024
Institute of Clinical Biometrics, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Background: Practice guidelines recommend patient management based on scientific evidence. Quality indicators gauge adherence to such recommendations and assess health care quality. They are usually defined as adverse event rates, which may not fully capture guideline adherence over time.
View Article and Find Full Text PDFNurse Educ
October 2024
Author Affiliations: The Ohio State University College of Nursing, Columbus, Ohio (Dr Hoying, Mss Terry and Gray-Bauer, and Dr Melnyk); and The University of Arizona College of Nursing, Tucson, Arizona (Dr Kelly).
Background: Nursing students experience significantly more stress related diseases when compared to non-nursing students, and the state of their mental health can result in short-term increased attrition rates and increased nursing shortages.
Purpose: A preexperimental pre-post study design was used to examine mental health and healthy behaviors among prenursing students.
Methods: Cohorts received the MINDSTRONG© program either in-person or virtually.
Med Sci Sports Exerc
October 2024
School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH.
Purpose: Motion capture technology is quickly evolving providing researchers, clinicians, and coaches with more access to biomechanics data. Markerless motion capture and inertial measurement units (IMUs) are continually developing biomechanics tools that need validation for dynamic movements before widespread use in applied settings. This study evaluated the validity of a markerless motion capture, IMU, and red, green, blue, and depth (RGBD) camera system as compared to marker-based motion capture during countermovement jumps, overhead squats, lunges, and runs with cuts.
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