Evidence map›Paper›PMID 42493715›Full record

ArticleSports medicine - open2026

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.

Yili Wang, Kenneth L Cameron, Michael A McCrea, Thomas McAllister, Steven Broglio, Mariel Lavieri, James T Eckner, CARE Consortium Investigators

Abstract read
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Article in Sports medicine - open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yili WangDepartment of Industrial & Operations Engineering, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-5529-3304
Kenneth L CameronKeller Army Hospital, West Point, NY, USA.ORCID http://orcid.org/0000-0002-6276-4482
Michael A McCreaDepartment of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA.
Thomas McAllisterDepartment of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
Steven BroglioSchool of Kinesiology, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-2282-9325
Mariel LavieriDepartment of Industrial & Operations Engineering, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0001-9164-0596
James T EcknerDepartment of Physical Medicine & Rehabilitation, University of Michigan, Ann Arbor, MI, USA. jeckner@med.umich.edu.ORCID http://orcid.org/0000-0001-9630-0048
CARE Consortium Investigators

Funding

U.S. Army Medical Research Acquisition Activity HT9425-24-1-0601
6 · The paper itself

Abstract

backgroundConcussion is a common but serious health concern among athlete and military populations. To improve understanding of concussion, the NCAA and U.S. Department of Defense established the Concussion Assessment, Research, and Education (CARE) Consortium, which collected comprehensive baseline assessment data from collegiate athletes and military service academy members. In this study, we used baseline assessments collected in service academy cadets and midshipmen to estimate the likelihood of concussion in the following academic year. Using eXtreme Gradient Boosting, we determined how the predictive accuracy of baseline assessments changes over the first year after testing and compared concussion risk prediction across two sub-populations (varsity/club sport athletes and intramural cadets) and two injury mechanisms (concussions occurring during military training/physical education classes versus sports-related concussions). Exploratory analyses also identified and ranked the most influential variables contributing to risk prediction, offering insights that could guide future risk assessment and prevention efforts.

resultsThe analytic dataset included 16,642 participants from four U.S. military academies between the 2015-2016 and 2019-2020 academic years after data preprocessing. We used eXtreme Gradient Boosting to predict concussion risk using baseline variables collected within the same academic year. Prediction accuracy decreased as time passed since the baseline assessment both in the overall model and within each sub-population and injury mechanism model, with the overall prediction dropping from 0.68 to less than 0.64. Concussion predictions were more accurate for intramural cadets and for injuries sustained during military training or physical education, compared to varsity/club sport athletes and sports-related concussions.

conclusionsGiven the significant time and resource investment required to conduct baseline concussion testing, baseline assessments being used to predict concussion risk should be performed as close to time periods of greatest risk as possible. Future research is needed to develop streamlined population- and injury mechanism-specific predictive models using a smaller set of key baseline measures to facilitate more frequent baseline testing to optimize risk prediction.

Identifiers

PMID42493715
PMCPMC13396043

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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.