Optimal cutoffs for the Montreal Cognitive Assessment vary by race and ethnicity
Bibliographic record
Abstract
INTRODUCTION: The Montreal Cognitive Assessment (MoCA), scored from 0 to 30, is used as a screening tool for mild cognitive impairment (MCI). The current cutoff (26) may not be optimal among minorities. METHODS: Data from the National Alzheimer's Coordinating Center Uniform Data Set March 2018 data freeze was used to calculate optimal cutoffs for detection of MCI and dementia by race/ethnic group and education. RESULTS: Of the 3895 individuals included, 80.7% were non-Hispanic White, 15.0% were non-Hispanic Black, and 4.2% were Hispanic. Optimal cutoffs for detection of MCI were 25 among non-Hispanic Whites, 24 among Hispanics, and 23 among non-Hispanic Blacks. Optimal cutoffs for detection of dementia were 19 among non-Hispanic Whites and 16 for both non-Hispanic Blacks and Hispanics. Lower educational attainment produced lower optimal cutoffs. DISCUSSION: Our findings suggest cutoffs may need to be stratified by race/ethnicity and education to ensure detecting MCI from normal and MCI from dementia.
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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 | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".