Validation of the Chinese Version of Montreal Cognitive Assessment Basic for Screening Mild Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effectiveness of the Chinese version of the Montreal Cognitive Assessment Basic (MoCA-BC) as a screening tool for detecting mild cognitive impairment (MCI) in Chinese elderly adults. DESIGN: Cross-sectional. SETTING: Huashan Hospital, Shanghai, China. PARTICIPANTS: Individuals with MCI (n = 264) and mild Alzheimer's disease (AD) (n = 160) were recruited from the Memory Clinic, Huashan Hospital; cognitively normal controls were recruited from Jinshan Community, Shanghai, China (n = 280). MEASUREMENTS: MoCA-BC scores. RESULTS: The MoCA-BC had good criterion-related validity (Pearson correlation coefficient MoCA-BC vs MMSE = 0.787) and reliable internal consistency (Cronbach alpha = 0.807). The optimal cutoff scores for MCI screening were 19 for individuals with no more than 6 years of education, 22 for individuals with 7 to 12 years of education, and 24 for individuals with more than 12 years of education. The MoCA-BC was superior to the MMSE for detecting MCI, with optimal sensitivity and specificity across all education groups using the above cutoff scores. CONCLUSION: The MoCA-BC is a reliable cognitive screening test across all education levels in Chinese elderly adults, with high acceptance and good reliability.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".