Optimal Cutoff Scores for Dementia and Mild Cognitive Impairment in the Brazilian Version of the Montreal Cognitive Assessment among the Elderly
Bibliographic record
Abstract
OBJECTIVE: To propose cutoff scores for the Brazilian version of the Montreal Cognitive Assessment (MoCA-BR) stratified by education in order to detect mild cognitive impairment (MCI) and mild Alzheimer's disease (AD) in the elderly. METHOD: A transversal study in health centers was performed on 159 elderly people with 4-12 years of education and 70 of their peers with over 12 years of schooling. The MoCA-BR cutoff scores for screening cognitive impairment were determined based on an ROC curve analysis. RESULTS: The ROC curve analysis indicated that cutoff scores under 20 were good for screening elderly people with cognitive impairment with more than 12 years of education, and scores under 21 were good for screening those with 4-12 years of education. CONCLUSIONS: MoCA-BR scores under 21 points (after adding 1 point to the elderly with ≤12 years of education) indicate a need to continue the diagnostic investigation with regular follow-ups.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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".