DOES GENDER 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 gender differences can alter performance on neuropsychological testing. PURPOSE To evaluate if gender differences affect the results on the McGill ACE. METHODS At preseason testing, female and male athletes from the varsity soccer and ice hockey teams underwent the McGill ACE testing. RESULTS Independent t-tests between female and male athletes on the McGill ACE results showed significant differences on the reverse digit test. Females scored worse than males on this test. CONCLUSION This study showed that gender difference exist on some neurocognitive tests. This may be related to differences within gender on visual-spatial strategies used to accomplish this type of test. This study reinforces the concept that ideally, results of neurocognitive testing should be compared to each athlete's baseline result and not to a control group. Further studies are needed to evaluate the impact of these results on evaluation of concussed female athletes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".