Normative data for the Montreal Cognitive Assessment (MoCA) in a population-based sample
Bibliographic record
Abstract
OBJECTIVE: To provide normative and descriptive data for the Montreal Cognitive Assessment (MoCA) in a large, ethnically diverse sample. METHODS: The MoCA was administered to 2,653 ethnically diverse subjects as part of a population-based study of cardiovascular disease (mean age 50.30 years, range 18-85; Caucasian 34%, African American 52%, Hispanic 11%, other 2%). Normative data were generated by age and education. Pearson correlations and analysis of variance were used to examine relationship to demographic variables. Frequency of missed items was also reviewed. RESULTS: Total scores were lower than previously published normative data (mean 23.4, SD 4.0), with 66% falling below the suggested cutoff (<26) for impairment. Most frequently missed items included the cube drawing (59%), delayed free recall (56%; <4/5 words), sentence repetition (55%), placement of clock hands (43%), abstraction items (40%), and verbal fluency (38%; <11 words in 1 minute). Normative data stratified by age and education were derived. CONCLUSION: These findings highlight the need for population-based norms for the MoCA and use of caution when applying established cut scores, particularly given the high failure rate on certain items. Demographic factors must be considered when interpreting this measure.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".