A - 53 Predicting Concussion Symptoms: Impact of Pre-Injury Risk Factors on Post-Concussion Symptom Severity in Football Players
Bibliographic record
Abstract
Abstract Purpose This study examines whether baseline mental health factors—anxiety, depression, somatization, concussion history, learning disorders, ADHD, and SCAT/BSI scores—predict post-concussion symptom severity in Canadian Football League (CFL) athletes. Findings aim to improve concussion management and targeted interventions. Method This longitudinal study collected data during pre-season medical evaluations and within 48 hours post-concussion (2017–2018) using SCAT3 for symptom severity and BSI-18 for psychological distress. Participants included 793 CFL players (90% of the league, all male, aged 21–37, M = 25.35, SD = 2.79). Results Baseline characteristics showed that 45.1% (n = 358) had a concussion history. ADHD was present in 10.0% (n = 79), learning disorders in 4.2% (n = 33), and mental health diagnoses in 1.0% (n = 8). Regression analysis identified significant predictors of post-injury anxiety: pre-injury BSI Somatization (β = −0.697, p = 0.004), pre-injury BSI Anxiety (β = 0.649, p = 0.022), and SCAT3 (β = 0.627, p < 0.001). Other variables, including pre-injury depression, ADHD, and learning disorders, were not significant. SCAT3 was the sole significant predictor of post-injury depression (β = 0.702, p < 0.001). Conclusions The findings of this study determined that subjective depressive symptoms on the BSI-18 appear to reasonably predict concussion outcome variables, suggesting the BSI-18 might be a useful screener for CFL athletes. This study has the potential to significantly impact a wide range of individuals and groups, including football coaches, sports medicine professionals, sports psychologists, football players, and possibly the general public.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".