Normative Data for the Montreal Cognitive Assessment (MoCA) in Mexican Adults: A Regression-Based Approach
Bibliographic record
Abstract
Background/Objectives: The Montreal Cognitive Assessment (MoCA) is a widely used cognitive screening tool designed to detect cognitive impairment. However, evidence indicates that the original cut-off score of 26 and the one-point correction for low education may not be appropriate across diverse populations. In Latin America, and particularly in Mexico, existing validation studies are scarce and limited by small sample sizes. The objective of this study was to examine the effects of age and education on MoCA performance in Mexican adults and to develop regression-based normative data for more accurate interpretation. Methods: MoCA performance of 2546 cognitively healthy participants aged 18–99 years from two public health institutions in Mexico City was analyzed. Inclusion criteria required preserved cognition, functionality, independence, and absence of conditions directly affecting brain health. The Spanish version 8.1 of the MoCA was administered. Age-adjusted normative values were obtained. Then, regression analyses were applied to generate age- and education-adjusted norms. Results: MoCA total scores correlated negatively with age and positively with education, while sex showed no significant effect. Regression-based norms revealed that identical raw total scores have different normative interpretations depending on age and education. Conclusions: This study provides the first regression-based MoCA norms for Mexican adults, demonstrating that both age and education exert a substantial influence on test performance. These norms enable a more precise, culturally sensitive interpretation than fixed cut-off scores and reduce the risk of misclassification in clinical and research settings.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".