Discriminative Ability of Montreal Cognitive Assessment Subtests and Items in Racial and Ethnic Minority Groups
Bibliographic record
Abstract
INTRODUCTION: The Montreal Cognitive Assessment (MoCA) is a popular screening tool for Mild Cognitive Impairment (MCI). The psychometric properties of the MoCA have not been widely examined in minority groups. We aimed to analyze the discriminate ability of subtests and items by race and ethnicity given gold-standard clinical diagnosis of cognitive status. METHODS: We analyzed data from the National Alzheimer Coordinating Center Uniform Data Set March 2018 data freeze. Stepwise regression was used to determine which subtests predicted cognitive status (normal cognition, MCI, or dementia), by race/ethnicity. Item discrimination and difficulty was calculated by race/ethnicity and cognitive status. RESULTS: In our sample (n=3895), with an average age of 69.7, 80.7% were non-Hispanic white, 15.0% were non-Hispanic black, and 4.2% were Hispanic. Among non-Hispanic whites all subtests, education, and age predicted clinician diagnosis, while visuospatial/executive, attention, language, delayed recall, and orientation subtests were predictive among non-Hispanic blacks and visuospatial/executive, delayed recall, and orientation subtests and education were predictive among Hispanics. Item discrimination and difficulty varied by race/ethnicity and cognitive status. CONCLUSIONS: By understanding the psychometric properties of MoCA subtests, we can focus on subtests that have higher discrimination and more diagnostic utility. Subtests should be further evaluated for use in screening of minority individuals.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".