Evidence map›Paper›PMID 40813735›Full record

ArticleAnnals of biomedical engineering2025

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.

Lauren L Czerniak, Gian-Gabriel P Garcia, Max W Genthe, Yueyun Xia, Mariel S Lavieri, Michael A McCrea, Thomas W McAllister, Paul F Pasquina, Spencer W Liebel, Steven P Broglio and 1 more

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Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Lauren L CzerniakDepartment of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave, Ann Arbor, MI, 48109, USA. czernL@umich.edu.ORCID http://orcid.org/0000-0003-4379-2004
Gian-Gabriel P GarciaH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Max W GentheDepartment of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave, Ann Arbor, MI, 48109, USA.
Yueyun XiaDepartment of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave, Ann Arbor, MI, 48109, USA.
Mariel S LavieriDepartment of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave, Ann Arbor, MI, 48109, USA.
Michael A McCreaDepartments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA.
Thomas W McAllisterDepartment of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA.
Paul F PasquinaDepartment of Physical Medicine & Rehabilitation, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
Spencer W LiebelDepartment of Neurology, University of Utah, Salt Lake City, UT, USA.
Steven P BroglioMichigan Concussion Center, University of Michigan, Ann Arbor, MI, USA.
CARE Consortium Investigators

Funding

National Science Foundation DGE 1841052U.S. Department of Defense W81XWH1420151U.S. Department of Defense W81XWH1820047
6 · The paper itself

Abstract

purposeSport-related concussion (SRC) is one of the most complex injuries in sports medicine, and many athletes experience one or multiple SRCs over their collegiate career. However, from the start to the end of an athlete's collegiate career, it remains unclear which methods can accurately predict the change, if any, in an athlete's reported symptom burden, cognitive status, and risk of psychological distress. It is also unclear which factors (e.g., number of concussions) have the greatest influence on this change.

methodsWe consider 3201 (1668 male, 1533 female) collegiate varsity athletes from the Concussion Assessment, Research, and Education (CARE) Consortium. Using five machine learning methods, we predict the change in athletes' reported symptom burden (i.e., Sport Concussion Assessment Tool [SCAT]), cognitive status (i.e., Standardized Assessment of Concussion [SAC]), and risk of psychological distress (i.e., Brief Symptom Inventory 18 [BSI-18]) over their collegiate careers.

resultsAll machine learning methods outperform a simple model that assumes no change with respect to mean squared error (i.e., machine learning helps avoid large prediction errors). We find that the initial baseline evaluation score is of greatest importance across all metrics of interest (e.g., SCAT/SAC/BSI-18). We also find that when an athlete has a poorer performance on the initial baseline evaluation, the prediction for the change in score is often larger and change for the better.

conclusionThis research provides insights on an athlete's change in critical areas of functioning over their collegiate career, and how machine learning can help improve the prediction of this change.

Indexed as

AthletesAthletic InjuriesBrain ConcussionCognitionPsychological DistressAdolescentFemaleHumansMachine LearningMaleSymptom BurdenYoung AdultCollegiate athletesConcussion managementMachine learningSport-related concussion

Identifiers

PMID40813735

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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.