P1‐528: MONTREAL COGNITIVE ASSESSMENT: DATA FOR SENIORS WITH HETEROGENEOUS EDUCATIONAL LEVELS IN BRAZIL
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) was created as a screening test to detect mild cognitive impairment (MCI). Studies have shown that the MoCA test has high diagnostic accuracy for MCI and dementia among individuals living in high income countries who frequently have around 12 years of education. The aim of the study was to provide MoCA norms and accuracy data for seniors within a lower education band, including illiterates. Data originated from an epidemiological study conducted in the municipality of Tremembé, Brazil. The Brazilian version of the 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). We have excluded 8 patients who had severe dementia with Clinical Dementia Rating (CDR) equal to 3. Among 102 demented participants, 92% were diagnosed as mild dementia with CDR = 1. MoCA norms were provided with the sample stratified into age and education bands. The total scores varied significantly according to age and education among the three diagnostic groups: CN, CIND and dementia. Total MoCA scores did not vary significantly between sex only in the dementia group (p=0.145). To distinguish CN from dementia considering education level < 5 years, the best MoCA cutoff was 15 points (sensitivity 93%, specificity 65%) and considering education ≥ 5 years, the MoCA cutoff was 16 points (sensitivity 86%, specificity 95%). To differentiate CN from CIND in participants with education < 5 years, the MoCA cutoff was 16 points (sensitivity 66%, specificity 65%) and with education ≥ 5 years, the MoCA cutoff was 19 points (sensitivity 58%, specificity 81%). The MoCA test did not have a good accuracy for detect CIND in this population with low educational level. Therefore, this tool could be used to detect dementia, especially in individuals with more than 5 years of education, with a lower cutoff score.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.002 | 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".