A-36Comparative MoCA Performance in Elderly Community Dwelling African, Hispanic, and Caucasian Americans Diagnosed with Dementia
Bibliographic record
Abstract
Objective: Comparative data for the Montreal Cognitive Assessment (MoCA) specific to ethnic minority groups drawn from the same population are limited (Rossetti et al., 2017). Therefore, this study was conducted to report descriptive and comparative data from patients at a single community memory clinic. Method: The MoCA was administered to 888 participants (55.7% females, 91.4% Caucasian, 5.4% African American, 3.2% Hispanic) as a cognitive screening measure prior to a neuropsychological evaluation in which they were diagnosed with dementia. The mean age was 78.56 years (SD = 6.17, range 45–85), and the average education level was 13.40 years (SD = 2.76). Results: A two-way ANOVA examined the role of race/ethnicity and sex on MoCA scores. Race had a significant main effect on MoCA score for those diagnosed with dementia (F(2,882) = 3.58, p = 0.03), while sex did not affect MoCA score (F(1,882) = 0.98, p = 0.32). Post-hoc tests revealed Caucasian MoCA scores (M = 17.62) were significantly different from African American scores (M = 16.13) (p = 0.018). There were no significant differences between Caucasian and Hispanic scores (M = 16.50) (p = 0.177) and Hispanic and African American scores (p = 0.712). There were no significant interactions. Conclusion: Interpreting scores that are not normed from a representative ethnic population may result in inaccurate diagnostic classification (Pedraza, et al., 2012). Findings suggest that previously established MoCA cutoff scores may not characterize performance accurately among ethnic minorities versus Caucasians who live in the same geographic area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".