The Montreal Cognitive Assessment as a Cognitive Screening Tool in Athletes
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is a cognitive screening tool known to accurately measure mild cognitive impairment (MCI) in many different neurological populations. OBJECTIVE: We aimed to determine whether a sport-related concussion (SRC) history and other concussion modifiers influence global cognitive function in high-performance athletes. METHODS: A cross-sectional study of 326 varsity and national team athletes aged 18-36 years was completed at the University of Calgary Sports Medicine Clinic, Calgary, Alberta, Canada. Logistic regression analysis was used to examine the association between the total MoCA score, MoCA subscales, and number of previous SRC, adjusting for age, sex, sport participation (SP), and concussion modifiers. RESULTS: Athletes with a history of three or more SRC were 5.36 times more likely to score less than 26/30 on the MoCA (the cutoff for MCI) compared to athletes with two or less SRC (p = 0.02). Males were 2.23 times more likely to have MCI than females (p = 0.0004). There was a significant relationship between the number of previous concussions and the MoCA subscales of attention (p = 0.05) and abstraction (p = 0.003). Age, SP, and concussion modifiers (migraine, depression, anxiety, and attention deficit and hyperactivity disorder) did not influence the relationship between MoCA and previous concussion history. CONCLUSION: In the appropriate clinical context, cognitive screening with the MoCA may benefit clinical care in athletes with multiple previous SRC, but should not replace a full neuropsychological assessment. Thus, further research is needed to compare the MoCA to full neuropsychological assessments in this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".