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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".