The Effect of High-Intensity Exertion on Sport Concussion Assessment Tool 5 Subcomponents
Bibliographic record
Abstract
Introduction: The Sport Concussion Assessment Tool 5 (SCAT5) is a commonly used assessment tool following a suspected sport-related concussion. However, little is known how SCAT5 subcomponent scores change following high-intensity exertion. Methods: Participants were recruited from the varsity womens rugby and mens and womens wrestling teams at the University of Calgary. The SCAT5 was administered prior to and following the 30-15 Intermittent Fitness test, where the primary outcome measures included: total symptom scores and severity, standardized assessment of concussion, neurological screening, and balance error during the modified balance error scoring system, as measured with the SCAT5. Wilcoxon signed-rank tests were utilized to evaluate differences in ordinal data between pre- and post-exertion. Bonferroni corrections were performed to account for multiple comparisons (0.05/9, p<0.006). Results: Thirty-seven varsity athletes (median age: 19 years, range: 17-23, 28 female) consented to participate in this investigation. The SCAT5 was administered by trained health care professionals a median of 20 minutes (range: 1–47 minutes) following exertion. No differences were found before and after the exertion test for Post-Concussion Symptom Score total number of symptoms (z=1.05, p=0.29), standardized assessment of concussion (z=-1.98, p=0.048), neurological screen (z=0.58, p=0.56), and modified Balance Error System Score (z=0.37, p=0.71). Conclusions: SCAT5 subcomponent scores were not significantly altered following high-intensity exertion in collision and combative varsity athletes. In agreement with previous literature, a 20 minute recovery period appears to be an acceptable timeframe for SCAT5 subcomponent scores to return to resting/baseline levels.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".