{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Claros CC"],"funding":["National Institute of Neurological Disorders and Stroke","U.S. Department of Defense","NINDS NIH HHS"],"pagination":["1971-1982"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12460441"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["55(8)"],"pubmed_abstract":["<h4>Background</h4>Emerging evidence indicates an elevated risk of post-concussion musculoskeletal injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated.<h4>Objective</h4>The purpose of this study was to model post-concussion musculoskeletal injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning.<h4>Methods</h4>A risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant's heath and athletic history, concussion injury and recovery-specific criteria, and outcomes from a diverse array of concussion assessments. The machine learning approach involved transforming variabl"],"journal":["Sports medicine (Auckland, N.Z.)"],"pubmed_title":["A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes."],"pmcid":["PMC12460441"],"funding_grant_id":["W81XWH-14-2-0151","1R21NS122033"],"pubmed_authors":["Qian W","Brockmeier AJ","Anderson MN","Claros CC","Buckley TA"],"additional_accession":[]},"is_claimable":false,"name":"A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.","description":"<h4>Background</h4>Emerging evidence indicates an elevated risk of post-concussion musculoskeletal injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated.<h4>Objective</h4>The purpose of this study was to model post-concussion musculoskeletal injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning.<h4>Methods</h4>A risk model was developed and tested on a dataset of 194 athletes (155 in the training set and 39 in the test set) with 135 variables entered into the analysis, which included participant's heath and athletic history, concussion injury and recovery-specific criteria, and outcomes from a diverse array of concussion assessments. The machine learning approach involved transforming variabl","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-06-03T19:43:24.392Z","creation":"2026-05-01T03:09:47.654Z"},"accession":"S-EPMC12460441","cross_references":{"pubmed":["40140234"],"doi":["10.1007/s40279-025-02196-4"]}}