ArticleSports medicine (Auckland, N.Z.)2021
Predicting Risk of Sport-Related Concussion in Collegiate Athletes and Military Cadets: A Machine Learning Approach Using Baseline Data from the CARE Consortium Study.
Article in Sports medicine (Auckland, N.Z.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- The application of machine learning in the prediction of sports injuries: systematic review and meta-analysis.Scientific reports · 2026Pooled it
- Prevention strategies and modifiable risk factors for concussion: a systematic review and meta-analysis for the Female, woman and girl/or Athlete Injury pRevention (FAIR) consensus.British journal of sports medicine · 2025Pooled it
- Clinical Outcome Following Concussion Among College Athletes with a History of Prior Concussion: A Systematic Review.Sports medicine - open · 2022Pooled it
- Effect of Time Since Baseline Data Collection, Mechanism of Injury, and Service Academy Sub-population on Concussion Risk Prediction in Military Service Academy Cadets and Midshipmen: An Observational Study.Sports medicine - open · 2026Article
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- Unsupervised learning-based optimization of college students' fundamental capacity evaluation method.Frontiers in physiology · 2026Article
- Prediction of Symptom Burden, Cognitive Status, and Risk of Psychological Distress in NCAA Athletes with Sport-Related Concussion(s): Findings from the NCAA-DoD CARE Consortium.Annals of biomedical engineering · 2025Article
- Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis.British journal of sports medicine · 2025Article
- Initial Mild Traumatic Brain Injury Characteristics and Recovery Patterns Among Females Across the United States Military Service Academies: A Report from the NCAA-DoD CARE Consortium.Annals of biomedical engineering · 2024Article
- Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea).Scientific reports · 2022Article
- System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm.BioMed research international · 2022Article
- Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion.Frontiers in sports and active living · 2022Article
- Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment.Sensors (Basel, Switzerland) · 2021Article
- Perceptual-Motor Efficiency and Concussion History Are Prospectively Associated With Injury Occurrences Among High School and Collegiate American Football Players.Orthopaedic journal of sports medicine · 2021Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 1 country.
Funding
Abstract
objectiveTo develop a predictive model for sport-related concussion in collegiate athletes and military service academy cadets using baseline data collecting during the pre-participation examination.
methodsBaseline assessments were performed in 15,682 participants from 21 US academic institutions and military service academies participating in the CARE Consortium Study during the 2015-2016 academic year. Participants were monitored for sport-related concussion during the subsequent season. 176 baseline covariates mapped to 957 binary features were used as input into a support vector machine model with the goal of learning to stratify participants according to their risk for sport-related concussion. Performance was evaluated in terms of area under the receiver operating characteristic curve (AUROC) on a held-out test set. Model inputs significantly associated with either increased or decreased risk were identified.
results595 participants (3.79%) sustained a concussion during the study period. The predictive model achieved an AUROC of 0.73 (95% confidence interval 0.70-0.76), with variable performance across sports. Features with significant positive and negative associations with subsequent sport-related concussion were identified. CONCLUSION(S): This predictive model using only baseline data identified athletes and cadets who would go on to sustain sport-related concussion with comparable accuracy to many existing concussion assessment tools for identifying concussion. Furthermore, this study provides insight into potential concussion risk and protective factors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.