Montreal cognitive assessment (MoCA): baseline evaluation of cognition in the athletic population
Bibliographic record
Abstract
Objective To assess baseline pre-season cognitive function in a young adult athletic population using the Montreal Cognitive Assessment Tool (MoCA). Design Baseline case series of normative data. Setting Sport Medicine Clinic at the University of Calgary, Alberta, Canada. Participants Male and female athletes (n=347) were recruited from the University of Calgary varsity athletic teams, Southern Alberta Institute of Technology college teams and Canadian National sports teams. Athletes (aged 18–40 years) underwent pre-season face-to-face standardised cognitive screening interviews. Interventions Baseline biographical information and a pre-season MoCA test were performed. Main Outcome Measurements The MoCA is a one page global cognitive assessment tool with a maximum score of 30 points. Results All subjects had grade 12 education or greater. Fifty-nine percent of the population was male. The average age was 22.02±3.49. The overall mean MoCA score was 26.62±2.20. One hundred and one subjects (29%) scored less than 26. For contact/collision sport athletes and non-contact sport athletes the mean MoCA scores were 26.41±2.28 and 26.97±2.01 respectively. There was a significant difference (p=0.018) between the contact/collision and non-contact sport athletes scores. Conclusions Approximately 29% of the athletic population surveyed had MoCA scores less than what is considered normal (<26). Contact/collision sport athletes had significantly lower MoCA's scores than non-contact sport athletes. Scores less than 26 have been associated with mild cognitive impairment and Alzheimer's dementia in other studies. Multiple factors (eg, previous concussion, non-documented head injuries, malingering, etc) may play a role in explaining the present findings. Competing interestsNone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".