P2‐576: SENSITIVITY AND SPECIFICITY OF THE MONTREAL COGNITIVE ASSESSMENT AMONG MINORITY POPULATIONS
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA), developed in 2005, is used as a screening tool for Mild Cognitive Impairment (MCI). The MoCA has a high sensitivity for detection of MCI in patients who would score as normal on the Mini Mental State Examination, however, its widely established cut-off of 26 for impairment may not be optimal for use in minority populations. Data come from the National Alzheimer's Coordinating Center's (NACC) Uniform Data Set (UDS) for visits conducted until the December 2017 data freeze. Participants who completed a MoCA at their baseline visit and reported being either non-Hispanic white, non-Hispanic black, or Hispanic were included in this analysis (n=3,539). SAS 9.4 was used to calculate descriptive statistics. Of the participants included in this analysis, 69.4 years old on the average, 81.2% were non-Hispanic white, 13.9% were non-Hispanic black, and 4.9% were Hispanic. Most had normal cognition (47.0%), as judged by a clinician, and the average raw MoCA score was 22.3. Using the cut-off of 26, the sensitivity of the MoCA was 87.4% in non-Hispanic whites, 94.3% in non-Hispanic blacks, and 93.8% in Hispanics; the specificity of the MoCA was 73.8% in non-Hispanic whites, 37.3% in non-Hispanic blacks, and 49.4% in Hispanics; the positive predictive value of the MoCA was 80.0% in non-Hispanic whites, 53.3% in non-Hispanic blacks, and 69.2% in Hispanics. Differences observed may be due to a lack of cultural equivalence of test items or to differential variability in MoCA score by race/ethnicity. The low specificity and positive predictive value of the MoCA observed in non-Hispanic blacks and Hispanics highlight a need to further investigate more appropriate cut-offs in minority groups to correctly identify cognitive impairment. Given the diversity if the United States population such norms will become increasingly important.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".