Comparison of the Montreal Cognitive Assessment and the Mini-Mental State Examination in detecting multi-domain mild cognitive impairment in a Chinese sub-sample drawn from a population-based study
Bibliographic record
Abstract
BACKGROUND: We examined the discriminant validity of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) in detecting multiple-domain mild cognitive impairment (md-MCI) in a Chinese sub-sample drawn from elderly population-based study. METHODS: This study included Chinese participants from the Epidemiology of Dementia in Singapore (EDIS) study aged ≥ 60 years who underwent cognitive screening with the Abbreviated Mental Test and Progressive Forgetfulness Questionnaire. Screen-positive participants subsequently underwent MoCA, MMSE, and a comprehensive formal neuropsychological battery. MCI was defined by Petersen's criteria and further classified into single-domain MCI (sd-MCI) and md-MCI. Area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs) was computed for the MoCA and the MMSE in detecting md-MCI. RESULTS: A total of 300 participants were recruited: 128 (42.7%) were diagnosed with no cognitive impairment (NCI), 47 (15.7%) with sd-MCI, and 83 (28.0%) with md-MCI. Forty-one participants were excluded, 7 (2.3%) had dementia, and 34 (11.3%) had only objective cognitive impairment without subjective complaints. Although the MoCA had a significantly larger AUC than the MMSE (0.94 (95% CI = 0.91-0.97) vs. 0.91 (95% CI = 0.86-0.95), p= 0.04), at optimal cut-off points, the MoCA (19/20) was equivalent to the MMSE (25/26) in detecting md-MCI (sensitivity: 0.80 vs. 0.87, specificity: 0.92 vs. 0.80). CONCLUSION: Both screening tests had good discriminant validity and can be used in detecting md-MCI in a sub-sample of Chinese drawn from a population-based study.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".