Validity of the <scp>C</scp>antonese <scp>C</scp>hinese <scp>M</scp>ontreal <scp>C</scp>ognitive <scp>A</scp>ssessment in <scp>S</scp>outhern <scp>C</scp>hinese
Bibliographic record
Abstract
AIM: The objective of the present study was to investigate the reliability and the validity of the Cantonese Chinese Montreal Cognitive Assessment (MoCA) as a brief screening tool of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) in Southern Chinese older adults. METHODS: Cognitively normal, aMCI and AD Cantonese-speaking Chinese older adults were recruited from a memory clinic and the community. The English MoCA was translated into Cantonese Chinese and then back-translated. We then evaluated the content validity, reliability, sensitivity and specificity of the Chinese Cantonese MoCA. RESULTS: We recruited 115 cognitively normal controls, 87 aMCI and 64 AD patients. Only education was positively correlated with the Cantonese MoCA score (r = 0.46, P < 0.001). The Chinese Cantonese MoCA had a high internal consistency with a Cronbach's alpha of 0.85. In the test-retest reliability assessment, the intraclass correlation coefficient (ICC) was 0.95 (P < 0.001). The ICC for the interrater reliability was 0.96 (P < 0.001). Receiving operating characteristic curve analyses showed an area under the curve of 0.85 and 0.99 for aMCI and AD, respectively (both P < 0.001). The optimal cut-off score for detection of aMCI was 22/23, which yielded a sensitivity and specificity of 78% and 73%, respectively. The optimal cut-off score for detection of AD was 19/20, which gave sensitivity and specificity of 94% and 92%, respectively. CONCLUSION: The Cantonese Chinese MoCA is a consistent and reliable instrument. In terms of its validity, the MoCA is better in the detection of AD than aMCI in Cantonese-speaking Chinese persons. It is only fair for the screening of aMCI.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".