Use of the MoCA in Detecting Early Alzheimer's Disease in a Spanish-Speaking Population with Varied Levels of Education
Bibliographic record
Abstract
BACKGROUND/AIMS: Performance on the Montreal Cognitive Assessment (MoCA) has been demonstrated to be dependent on the educational level. The purpose of this study was to identify how to best adjust MoCA scores and to identify MoCA items most sensitive to cognitive decline in incipient Alzheimer's disease (AD) in a Spanish-speaking population with varied levels of education. METHODS: We analyzed data from 50 Spanish-speaking participants. We examined the pattern of diagnosis-adjusted MoCA residuals in relation to education and compared four alternative score adjustments using bootstrap sampling. Sensitivity and specificity analyses were performed for the raw and each adjusted score. The interval reliability of the MoCA as well as item discrimination and item validity were examined. RESULTS: We found that with progressive compensation added for those with lower education, unexplained residuals decreased and education-residual association moved to zero, suggesting that more compensation was necessary to better adjust MoCA scores in those with a lower educational level. Cube copying, sentence repetition, delayed recall, and orientation were most sensitive to cognitive impairment due to AD. CONCLUSION: A compensation of 3-4 points was needed for <6 years of education. Overall, the Spanish version of the MoCA maintained adequate psychometric properties in this population.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".