Evaluating the Beijing Version of Montreal Cognitive Assessment for Identification of Cognitive Impairment in Monolingual Chinese American Older Adults
Bibliographic record
Abstract
Objective: This study aims to evaluate the performance of a Chinese version of the Montreal Cognitive Assessment (MoCA) as a screener to detect mild cognitive impairment (MCI) and dementia from normal cognition in the monolingual Chinese-speaking immigrant population. Method: A cohort of 176 Chinese-speaking older adults from the National Alzheimer’s Coordinating Center Uniform Data Set is used for analysis. We explore the impact of demographic variables on MoCA performance and calculate the optimal cutoffs for the detection of MCI and dementia from normal cognition with appropriate demographic adjustment. Results: MoCA performance is predicted by age and education independent of clinical diagnoses, but not by sex, years of living in the U.S., or primary Chinese dialect spoken (i.e., Mandarin vs. Cantonese). With adjustment and stratification for education and age, we identify optimal cutoff scores to detect MCI and dementia, respectively, in this population. These optimal cutoff scores are different from the established scores for non-Chinese-speaking populations residing in the U.S. Conclusions: Our findings suggest that the Chinese version of MoCA is a valid screener to detect cognitive decline in older Chinese-speaking immigrants in the U.S. They also highlight the need for population-based cutoff scores with appropriate considerations for demographic variables.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".