Validation and Optimal Cut-Off Scores of the Bahasa Malaysia Version of the Montreal Cognitive Assessment (MoCA-BM) for Mild Cognitive Impairment among Community Dwelling Older Adults in Malaysia (Keesahan dan Skor Titik Potong Optimum Versi Bahasa Malaysia Penilaian Kognitif Montreal (MoCA-BM) untuk Kecelaan Kognitif Ringan dalam Kalangan Komuniti Rumah Warga Tua di Malaysia)
Bibliographic record
Abstract
The goal of this study was to examine the reliability and validity of the Bahasa Malaysia version of the Montreal cognitive assessment (MoCA-BM) and to determine its optimal cut-off score among older adults with mild cognitive impairment (MCI), after adjustments for age, gender, levels of education, physical functioning and depressive symptoms. A total of 2237 community dwelling older adults aged 60 years and above were randomly selected for the study, excluding those with MMSE score below 14. Instruments administered were the MoCA-BM, the Malay Mini-Mental State Examination (MMMSE), the Rey Auditory Verbal Learning Test (RAVLT), the Digit Span and the Digit Symbol subtests of the Wechsler Adult Intelligence Scale (WAIS), activities of daily living (ADL) and the Geriatric Depression Scale (GDS). MCI were determined using the Petersen’s 2014 criteria as the gold standard. SPSS version 22 was used for reliability and validity analysis and optimal cut-off score detection. Cronbach’s α of the MoCA-BM was 0.691 and concurrent validity was high between MoCA-BM and MMMSE scores (r=0.741). Optimal cut-off point for MoCA-BM to detect MCI among older adults in Malaysia was 17/18, with sensitivity of 68.2% and specificity of 61.3%. Using this cut-off, 38.9% of participants were detected to be at risk of MCI. In conclusion, MoCA-BM is a reliable and valid screening instrument for MCI among Malaysian elderly community. The newly derived optimal cut-off for MCI is much lower than the original MoCA with modest ability to discriminate between normal and MCI older adults in the community.
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 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".