MoCA Test: normative and diagnostic accuracy data for seniors with heterogeneous educational levels in Brazil
Bibliographic record
Abstract
ABSTRACT The Montreal Cognitive Assessment (MoCA) has been described as a good tool to detect cognitive impairment. The ideal MoCA cutoff score is still under debate. The aim was to provide MoCA norms and accuracy data for seniors with a lower education level, including illiterates. Methods: Data originated from an epidemiological study conducted in the municipality of Tremembe, Brazil. The Brazilian MoCA test was applied as part of the cognitive assessment in all participants. Of the 630 participants, 385 were classified as cognitively normal (CN) and were included in the normative data set, 110 individuals were diagnosed with dementia and 135 were classified as having cognitive impairment no dementia (CIND). Results: The total scores varied significantly according to age and education among the three diagnostic groups: CN, CIND and dementia (p < 0.001). To distinguish participants with CN from dementia, the best MoCA cutoff was 15 points (sensitivity 90%, specificity 77%) and to differentiate those with CN from CIND, the MoCA cutoff was 19 points (sensitivity 84%, specificity 49%). Those scores varied according to education level. Conclusions: The MoCA test did not have a high accuracy for detecting CIND in the population with a low educational level. Nevertheless, this tool may be used to detect dementia, especially in individuals with more than five years of education, if a lower cutoff score is adopted.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".