Validation of Malay Version of Montreal Cognitive Assessment in Patients with Cognitive Impairment
Bibliographic record
Abstract
Background: Montreal Cognitive Assessment (MoCA) has been shown to be a sensitive tool for cognitive assessment. There are high proportion of Malaysian elderly with limited proficiency in English language. Malay language is a more familiar language across the multiracial population of Malaysia. Objective: The aim of this study is to validate the Malay version of Montreal Cognitive Assessment (MMoCA) in cognitive impairment patients. Methods: Elderly aged 60 years and above were recruited by using convenient sampling method from 4 government hospitals. Subjects were categorized into normal control group versus patients group with cognitive impairment (Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI)). All subjects completed MMoCA & MMSE – Malay version, followed by a second assessment, which involved Clinical Dementia Rating (CDR), clinical neurological and psychiatry assessment. Results: Total of 66 subjects was enrolled in the study, 44 were normal control, 14 with AD, 8 with MCI. MMoCA is better than MMSE-Malay in differentiating CDR 0 from CDR > 0. With the cut off point of less than 22, MMoCA has the sensitivity of 0.824 and specificity of 0.818 to detect cognitive impairment. Whereas MMSE-Malay only has sensitivity of 0.765 and specificity of 0.636 with the cut off point of less than 27. Conclusion: The MMoCA is a validated and useful cognitive screening instrument in patients with cognitive impairment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".