DOES EXERTION MODIFY RESULTS ON THE MCGILL ABBREVIATED CONCUSSION EVALUATION (MCGILL ACE)?
Bibliographic record
Abstract
Condensed neuropsychological evaluation profiles of concussion have been developed in recent years. The McGill ACE is an example of one of these abbreviated concussion evaluations. The McGill ACE offers the advantage that it can be administered in five minutes by a trained individual, not necessarily a neuropsychologist. Previous studies have questioned if physiological changes resulting from exercise can alter performance on neuropsychological testing. PURPOSE: To evaluate the effect of exertion on the McGill ACE results when compared to rest testing at baseline. METHODS: On preseason exam, male athletes from the varsity football and ice hockey teams underwent the McGill ACE testing at resting state. Months later (to eliminate any practice/learning effect), 15 of the same athletes who did not have a concussion during the season underwent repeat baseline testing during exertional state. To mimic conditions which would occur in their sport for a short duration, players ran on the treadmill to attained 80% of maximal heart rate (based on the formula 220-age). Once such heart rate was attained for 1 minute, exertion was considered to have begun and players ran for 4 minutes, ensuring the heart rate stayed in same the range (determined by heart monitor). Following this, within 1 minute from the stop, athletes underwent McGill ACE testing. The score obtained on the McGill ACE after exertion was then compared to the athlete's own baseline result using a paired t-test. RESULTS: Paired t-test between McGill ACE results on baseline testing and after 4 minutes of exertion on the treadmill at a mean of 84% of maximum heart rate showed no significant difference. CONCLUSION: This study showed that exertion had no effect on baseline McGill ACE score thereby increasing its utility as a concussion baseline evaluation. Future studies will address potential changes with exertion post-concussion.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".