P-1023 - A chinese version of montreal cognitive assessment as a brief screening tool for mild cognitive impairment: a population-based study
Bibliographic record
Abstract
The sensitivity and specificity of a Chinese version of Montreal Cognitive Assessment (MoCA-CV) for Screening mild cognitive impairment (MCI) were examined. MoCA-CV and MMSE together with a battery of neuropsychological tests were administered to 1056 elders who were recruited from three regions of Beijing (i.e. new town, old town, rural areas). Twenty-six participants were diagnosed with dementia, 118 were MCI by psychiatrists and neurologists, and 875 were considered to be cognitively normal based on independent neuropsychological tested. Using the recommended cut-off score of 26, the MoCA-CV had an excellent sensitivity of 90.4% to detect MCI from controls, while the specificity was relatively poor (32.0%). Using a lower cut-off score of 22, the MoCA-CV exhibited the optimal balanced sensitivity of 68.7% and specificity of 65.2%. Among all the seven cognitive sub-domains, delayed recall performed the best differentiation of MCI from normal controls. Item analysis showed the internal consistency was relatively low in both naming and sentence repetition tasks which related with the characteristics of Chinese language and culture. Region differences were not significant when such confounding demographic variables as age, gender and education were controlled. The MoCA-CV is a brief and effective screening tool for detecting MCI. However, presumably due to linguistic and culture issues and the lower education level of Chinese elders, the MoCA-CV did not show better screening capability than MMSE as expected. Some items may need further adaptation to accommodate the language, culture and education characteristics of Chinese participants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".