The Montreal Cognitive Assessment—Basic: A Screening Tool for Mild Cognitive Impairment in Illiterate and Low‐Educated Elderly Adults
Bibliographic record
Abstract
OBJECTIVES: To assess the validity of a newly developed cognitive screening tool, the Montreal Cognitive Assessment-Basic (MoCA-B), in screening for mild cognitive impairment (MCI) in elderly adults with low education and varying literacy. DESIGN: Cross-sectional. SETTING: Community hospital in Bangkok, Thailand. PARTICIPANTS: Cognitively normal controls (n = 43) and individuals with MCI according to the National Institute on Aging-Alzheimer's Association work group criteria (n = 42) aged 55 to 80 with less than 5 years of education. MEASUREMENTS: MoCA-B scores. RESULTS: Mean MoCA-B scores were 26.3 ± 1.6 for illiterate controls and 21.3 ± 3.8 for illiterate participants with MCI (P < .001) and 26.6 ± 2.0 for literate controls and 23.0 ± 2.1 for literate participants with MCI (P < .001). MoCA-B scores did not differ significantly according to literacy, and multiple regression suggested no association with age or education. The optimal cutoff score of 24 out of 25 yielded 81% sensitivity and 86% specificity for MCI (area under the receiver operating characteristic curve = 0.90, P < .001). Test-retest reliability was 0.91 (P < .001), and internal consistency was 0.82. Administration time was 15 to 21 minutes. CONCLUSION: The MoCA-B appears to have excellent validity and addresses an unmet need by accurately screening for MCI in poorly educated older adults regardless of literacy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".