Staging of Dementia Severity With the Hong Kong Version of the Montreal Cognitive Assessment (HK-MoCA)’s
Bibliographic record
Abstract
BACKGROUND: The Hong Kong version of Montreal Cognitive Assessment (HK-MoCA) has been used to screen for dementia, but it has not been validated to delineate the stages of Alzheimer disease (AD). This study aimed to determine the cut-off score ranges for mild, moderate, and severe AD. METHODS: The HK-MoCA score was matched against the Clinical Dementia Rating on 155 patients with AD. Investigators performing the HK-MoCA and Clinical Dementia Rating were blinded to each other. Receiver-operating characteristic analysis was used to determine the cut-off scores between different stages of AD (mild, moderate, and severe stage). A secondary analysis with adjustments for age and education received were also performed. RESULT: The cut-off score in HK-MoCA was ≤4 for those with severe AD (sensitivity 84.4%, specificity 91.9%, area under curve=0.92, P<0.001) and 5 to 9 for those with moderate AD (sensitivity 86.3%, specificity of 93.3%, area under curve=0.953, P<0.001). With adjustments for age and education, the cut-off score for moderate AD was adjusted to 5 to 8, whereas the cut-off score for severe AD remained unchanged. CONCLUSIONS: The severity of AD could be delineated using the HK-MoCA for the Cantonese-speaking population in Hong Kong, and the effect of education on the cut-off score needs further investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".