Montreal Cognitive Assessment Performance among Community-Dwelling African Americans
Bibliographic record
Abstract
OBJECTIVE: To report descriptive and normative data for the Montreal Cognitive Assessment (MoCA) in a population-based African American sample. METHOD: The MoCA was administered to 1,419 African American participants (mean age 49.89 years, range 18-75, 64% female). After excluding those with subjective cognitive complaints (n = 301), normative data were generated by education and overlapping age ranges (n = 1,118). Pearson correlations and analysis of variance were used to examine the relationship to demographic variables, and frequency of missed items was reviewed. RESULTS: Total MoCA scores (mean 22.3, SD 3.9) were lower than previously published normative data derived from an elderly Caucasian Canadian population with 80% falling below the suggested cutoff (<26) for impairment. Several MoCA items were missed by a large portion of the sample, including cube drawing (72%), delayed free recall (66% <4/5 words), sentence repetition (63%), and abstraction items (45%). CONCLUSION: This is the first study to examine normative performance on the MoCA specific to community-dwelling African Americans. Findings suggest that certain aspects of this measure and previously established cutoff scores may not be well-suited for some populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".