Validity and reliability of two alternate versions of the Montreal Cognitive Assessment (Hong Kong version) for screening of Mild Neurocognitive Disorder
Bibliographic record
Abstract
OBJECTIVE: Repeated testing using the Montreal Cognitive Assessment (MoCA) increases risks for practice effects which may bias measurements of cognitive change. The objective of this study is to develop two alternate versions of the MoCA (Hong Kong version; HK-MoCA) and to investigate the validity and reliability of the alternate versions in patients with DSM-5 Mild Neurocognitive Disorder (Mild NCD) and cognitively healthy controls. METHODS: Concurrent validity and inter-scale agreement were examined by Pearson correlation of the total scores between the original and alternate versions and the Bland-Altman Method. Criterion validity of the two alternate versions in differentiating patients with Mild NCD was tested using receiver operating characteristic curve (ROC) analysis. One-month test-retest and inter-rater reliability were examined in 20 participants. Internal consistency of the alternate versions was measured by the Cronbach's α. RESULTS: 30 controls (age 73.4 [4.5] years, 60% female) and 30 patients (age 75.4 [5.5] years, 73% female) with Mild NCD were recruited. Both alternate versions significantly correlated with the original version (r = 0.79-0.87, p<0.001). Mean differences of 0.17 and -0.40 points were found between the total scores of the alternate with the original versions with a consistent level of agreement observed throughout the range of cognitive abilities. Both alternate versions significantly differentiated patients with Mild NCD from healthy controls (area under ROC 0.922 and 0.724, p<0.001) and showed good one-month test-retest reliability (intra-class correlation [ICC] = 0.92 and 0.82) and inter-rater reliability (ICC = 0.99 and 0.87) and high internal consistency (Cronbach α = 0.79 and 0.75). CONCLUSION: The two alternate versions of the HK-MoCA are useful for Mild NCD screening.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".