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Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion
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Optimizing Concussion Care Seeking: Using Machine Learning to Predict Delayed Concussion Reporting.

Emily Kroshus-Havril1,2, Daniel D Leeds3,2, Thomas W McAllister4,2

  • 1Center for Child Health, Behavior, and Development, Seattle Children's Research Institute & Department of Pediatrics, University of Washington, Seattle, Washington, USA.

The American Journal of Sports Medicine
|August 5, 2024
PubMed
Summary
This summary is machine-generated.

Early concussion reporting is crucial but often delayed. Observable symptoms like altered mental status predict immediate reporting, while institutional factors like revenue also play a role in timely concussion diagnosis.

Keywords:
care seekingcollegeconcussionmachine learning

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Area of Science:

  • Sports Medicine
  • Neurology
  • Public Health

Background:

  • Timely medical attention post-concussion is vital for minimizing symptom duration and severity.
  • Concussions are frequently undiagnosed or experience delayed diagnosis due to the often invisible nature of symptoms like headache and dizziness.
  • Understanding factors influencing concussion reporting is key to developing effective interventions.

Purpose of the Study:

  • To identify individual and institutional factors predicting immediate versus delayed reporting of concussions.
  • To enhance early concussion identification and diagnosis strategies.

Main Methods:

  • Secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study.
  • Utilized machine learning techniques on data from 3213 collegiate athletes and military cadets across 30 institutions.
  • Models predicted immediate injury reporting based on individual and institutional characteristics.

Main Results:

  • Individual factors, particularly altered mental status and loss of consciousness, were strong predictors of immediate concussion reporting.
  • Institutional factors, including athletic department revenue and athlete-to-trainer ratios, also influenced reporting immediacy.
  • Adding institutional factors improved prediction accuracy by 1-6 percentage points over individual factors alone.

Conclusions:

  • Observable individual symptoms are critical for immediate concussion reporting.
  • Institutional resource allocation and staffing levels may impact timely concussion diagnosis and reporting.
  • Further research is needed to understand institutional influences on reporting, and machine learning models require expansion for better concussion reporting prediction.