Validation of the Malay Version of Addenbrooke’s Cognitive Examination III in Detecting Mild Cognitive Impairment and Dementia
Bibliographic record
Abstract
Background/Aims: This study aimed to investigate the validity and reliability of the Malay version of Addenbrooke’s Cognitive Examination III (ACE-III) for detecting mild cognitive impairment (MCI) and dementia. Methods: A total of 152 participants (dementia = 53, MCI = 38, controls = 61) were recruited from two teaching hospitals. The Malay version of ACE-III was translated following the standard guidelines for cross-cultural adaptation of measure. All the participants were assessed with the Malay version of ACE-III and Mini-Mental State Examination (MMSE). Results: The reliability of the Malay version of ACE-III was good with Cronbach’s α coefficient of 0.829 and intraclass correlation coefficient of 0.959. There was a strong positive correlation between the Malay version of ACE-III and MMSE (r = 0.806). Age (r = –0.335) and years of education (r = 0.536) exerted a significant correlation with total score performance. The cutoff score to discriminate dementia from healthy controls was 74/75 (sensitivity = 90.6%, specificity = 82.0%) whereas to discriminate MCI, the cutoff score was 77/78 (sensitivity = 63.2%, specificity = 63.9%). The diagnostic accuracy of ACE-III was higher than that of MMSE in the detection of dementia (area under the curve: ACE-III = 0.929 vs. MMSE = 0.915). Conclusions: The Malay version of ACE-III demonstrated to be a reliable and valid screening tool for dementia.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".