Montreal Cognitive Assessment and Modified Mini Mental State Examination in African Americans
Bibliographic record
Abstract
Background. Sparse data limit the interpretation of Montreal Cognitive Assessment (MoCA) scores, particularly in minority populations. Additionally, there are no published data on how MoCA scores compare to the widely used Modified Mini Mental State Examination (3MSE). We provide performance data on the MoCA in a large cohort of African Americans and compare 3MSE and MoCA scores, providing a "crosswalk" for interpreting scores. Methods. Five hundred and thirty African Americans with type 2 diabetes were enrolled in African American-Diabetes Heart Study-MIND, a cross-sectional study of cognition and structural and functional brain imaging. After excluding participants with possible cognitive impairment (n = 115), mean (SD) MoCA and 3MSE scores are presented stratified by age and education. Results. Participant mean age was 58.2 years (range: 35-83); 61% were female; and 64.9% had >12 years of education. Mean (SD) 3MSE and MoCA scores were 86.9 (8.2) and 19.8 (3.8), respectively. 93.5% of the cohort had a "positive" screen on the MoCA, scoring <26 (education-adjusted), compared with 47.5% on the 3MSE (cut-point < 88). A 3MSE score of 88 corresponded to a MoCA score of 20 in this population. Conclusion. The present data suggest the need for caution when applying proposed MoCA cutoffs to African Americans.
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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 | 0.004 |
| 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".