Montreal Cognitive Assessment for Screening Mild Cognitive Impairment: Variations in Test Performance and Scores by Education in Singapore
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) was developed as a screening instrument for mild cognitive impairment (MCI). We evaluated the MoCA's test performance by educational groups among older Singaporean Chinese adults. METHOD: The MoCA and Mini-Mental State Examination (MMSE) were evaluated in two independent studies (clinic-based sample and community-based sample) of MCI and normal cognition (NC) controls, using receiver operating characteristic curve analyses: area under the curve (AUC), sensitivity (Sn), and specificity (Sp). RESULTS: The MoCA modestly discriminated MCI from NC in both study samples (AUC = 0.63 and 0.65): Sn = 0.64 and Sp = 0.36 at a cut-off of 28/29 in the clinic-based sample, and Sn = 0.65 and Sp = 0.55 at a cut-off of 22/23 in the community-based sample. The MoCA's test performance was least satisfactory in the highest (>6 years) education group: AUC = 0.50 (p = 0.98), Sn = 0.54, and Sp = 0.51 at a cut-off of 27/28. Overall, the MoCA's test performance was not better than that of the MMSE. In multivariate analyses controlling for age and gender, MCI diagnosis was associated with a <1-point decrement in MoCA score (η(2) = 0.010), but lower (1-6 years) and no education was associated with a 3- to 5-point decrement (η(2) = 0.115 and η(2) = 0.162, respectively). CONCLUSION: The MoCA's ability to discriminate MCI from NC was modest in this Chinese population, because it was far more sensitive to the effect of education than MCI diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".