Psychometric properties of the Montreal Cognitive Assessment (MoCA) in healthy participants aged 18–70
Bibliographic record
Abstract
Objectives: The Montreal Cognitive Assessment (MoCA) is a cognitive screen, available in three alternate versions. Aims of the current study were to examine the effects of age, education and intelligence on MoCA performance and to determine the alternate-form equivalence and test–retest reliability of the MoCA, in a group of healthy participants.Method: In 210 participants, two MoCA versions and an estimator for premorbid intelligence were administered at two time points.Results: Age, education and estimated premorbid intelligence correlated significantly with the total score (MoCA-TS) and the Memory Index Score (MoCA-MIS). Systematic differences between MoCA version 7.1 and alternate versions 7.2 and 7.3 were only found for the items animal naming, abstract reasoning and sentence repetition. Test–retest reliability of the MoCA-TS was good between 7.1 and 7.2 (ICC: 0.64) and excellent between 7.1 and 7.3 (ICC: 0.82). For the MoCA-MIS, coefficients were poor (ICC: 0.32) to fair (ICC: 0.48), respectively.Conclusion: Adequate norms are needed that take the effects of age, education and intelligence on MoCA performance into account. All three MoCA versions are largely equivalent based on MoCA-TS and the test–retest reliabilities show that this score is suitable to monitor cognitive change over time. Comparisons of the domain-specific scores should be interpreted with caution.Key pointsThe MoCA total score is a reliable cognitive measure.All three MoCA versions are largely equivalent.Age, education and intelligence are predictors of MoCA performance in healthy participants.Future studies should focus on collecting normative data for age, education and intelligence for use in clinical practice.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".