Examination of the utility of the gold standard cutoff score for the Montreal Cognitive Assessment (MoCA) in Chinese American older adults: A pilot study
Bibliographic record
Abstract
Abstract Background The gold standard cutoff (<26) for the Montreal Cognitive Assessment (MoCA) was developed using a predominantly White and English‐speaking sample. However, the diagnostic value of this cutoff score in a culturally and linguistically diverse population, such as older Chinese Americans, has been understudied and remains inconclusive. As such, this pilot study aimed to examine specific psychometric properties of the standard cutoff score on Chinese language versions of the MoCA for detecting mild cognitive impairment (MCI) and dementia in Chinese American older adults. Method The Chinese language versions of the MoCA was administered to 88 older Chinese Americans (19 MCI, 26 dementia, and 43 normal cognition ‐ NC) from Mount Sinai’s Alzheimer’s Disease Research Center (ADRC) in Mandarin/Cantonese by trained psychometricians. All participants self‐reported as Chinese Americans who were primarily Cantonese‐ or Mandarin‐speaking. Exclusion criteria included: not proficient in Cantonese/Mandarin and history of psychiatric disorder(s). Diagnoses were assigned at a consensus conference. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to detect MCI and dementia from NC and were then compared to previously published findings in a primarily White English‐speaking sample. Result The gold standard cutoff yielded poor sensitivity to detect MCI and dementia from participants with NC (both 0.47) in this study cohort, but comparable specificity (MCI vs. NC = 0.84; dementia vs. NC = 1.00) to previously published findings (MCI: sensitivity = 0.90, specificity = 0.87; dementia: sensitivity = 1.00, specificity = 0.87). NPV was poor (MCI: NPV = 0.41; dementia: NPV = 0.53), while PPV was similar in detecting both MCI and dementia (MCI: PPV = 0.87; dementia: PPV = 1.00) to previous findings (MCI: PPV = 0.89, NPV = 0.91; dementia: PPV = 0.89, NPV = 1.00). Conclusion The MoCA cut off score had poor sensitivity and NPV, but excellent specificity and PPV in detecting MCI and AD in our cohort. These findings suggest that the traditional cutoff score may not be appropriate for screening of cognitive impairment in older Chinese Americans. As the most frequently administered cognitive screening tool, the MoCA cutoff score should be adjusted for older Chinese Americans with limited to no English proficiency. Future studies in a larger cohort of older Chinese Americans are needed to establish a true gold standard for this specific population.
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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 | 0.011 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".