The Beijing version of the montreal cognitive assessment as a brief screening tool for mild cognitive impairment: a community-based study
Bibliographic record
Abstract
BACKGROUND: A cross-sectional validation study was conducted in several urban and rural communities in Beijing, China, to evaluate the effectiveness of the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) as a screening tool to detect mild cognitive impairment (MCI) among Chinese older adults. METHODS: The MoCA-BJ and the Mini-Mental State Examination (MMSE) were administered to 1001 Chinese elderly community dwellers recruited from three different regions (i.e., newly developed, old down-town, and rural areas) in Beijing. Twenty-one of these participants were diagnosed by experienced psychiatrists as having dementia, 115 participants were diagnosed as MCI, and 865 participants were considered to be cognitively normal. To analyze the effectiveness of the MoCA-BJ, we examined its psychometric properties, conducted item analyses, evaluated the sensitivity and specificity of the scale, and compared the scale with the MMSE. Demographic and regional differences among our subjects were also taken into consideration. RESULTS: Under the recommended cut-off score of 26, the MoCA-BJ demonstrated an excellent sensitivity of 90.4%, and a fair specificity (31.3%). The MoCA-BJ showed optimal sensitivity (68.7%) and specificity (63.9%) when the cut-off score was lowered to 22. Among all the seven cognitive sub-domains, delayed recall was shown to be the best index to differentiate MCI from the normal controls. Regional differences disappeared when the confounding demographic variables (i.e., age and education) were controlled. Item analysis showed that the internal consistency was relatively low in both naming and sentence repetition tasks, and the diagnostic accuracy was similar between the MoCA-BJ and the MMSE. CONCLUSIONS: In general, the MoCA-BJ is an acceptable tool for MCI screening in both urban and rural regions of Beijing. However, presumably due to the linguistic and cultural differences between the original English version and the Chinese version of the scale, and the lower education level of Chinese older adults, the MoCA-BJ is not much better than the MMSE in detecting MCI, at least for this study sample. Further modifications to several test items of the MoCA-BJ are recommended in order to improve the applicability and effectiveness of the MoCA-BJ in MCI screening among the Chinese 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".