Montreal Cognitive Assessment (MoCA): An update normative study for the Portuguese population
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a brief cognitive screening instrument that is known for its good psychometric properties and sensitivity to detect mild cognitive impairment (MCI). After ten years, it became relevant to update the previous Portuguese normative study due to changes in the population and some limitations present in the study itself. The study sample was composed of 860 cognitively healthy adults, stratified according to verified distribution of the Portuguese population across several sociodemographic variables. All participants completed a neuropsychological assessment battery, in which the MoCA was included. The analysis of the relationships between the sociodemographic variables and the MoCA show that age and educational level had a significant effect on MoCA scores, with educational level being the better predictor. These results foster the consideration of age and educational level in the development of normative data. The present study contributes to a reliable update of the normative data of MoCA. The new age groups and more stratified norms comply with the natural changes on the Portuguese population, providing an increase of power and clinical accuracy. The presented norms consider the cognitive domains subscores, consequently improving the comprehension and utility of the results obtained from the MoCA test.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| 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".