Effectiveness of Montreal Cognitive Assessment for the diagnosis of mild cognitive impairment and mild Alzheimer’s disease in Singapore
Bibliographic record
Abstract
INTRODUCTION: Mild cognitive impairment (MCI) is an important clinical entity with significant management implications. However, traditional screening tools lack the sensitivity needed to detect amnestic MCI (MCI-A). Montreal Cognitive Assessment (MoCA) has yet to be validated for the diagnosis of MCI in a multiracial society such as Singapore. We thus aimed to study the effectiveness of MoCA for the diagnosis of MCI-A in the Singapore population. METHODS: Data on patients with MCI-A and mild Alzheimer's disease (AD) was obtained from a prospectively collected clinical database between January 2008 and January 2011. Patients with no cognitive impairment (NCI) were recruited from among the spouses and friends of patients attending the memory clinic. RESULTS: There were a total of 212 participants (103 NCI, 49 MCI-A, 60 mild AD). For the diagnosis of MCI-A, a MoCA score of < 26 for patients with ≤ 10 years of education, and a score of < 27 for patients with > 10 years of education provided a sensitivity of > 94%. For the diagnosis of mild AD, a MoCA score of < 24 for patients with ≤ 10 years of education, and a score of < 25 for patients with > 10 years of education provided a sensitivity of > 85%. CONCLUSION: In the Singapore population, we recommend cutoff scores of 26/27 and 24/25 be used to detect MCI-A and mild AD, respectively, when using MoCA. For patients with ≤ 10 years of education, a +1 point correction is needed.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".