The Hong Kong version of Montreal Cognitive Assessment for the Visually Impaired (HKMoCA-VI): Proposed cut-off and cognitive functioning survey of visually impaired elderly in residential homes
Bibliographic record
Abstract
BACKGROUND: Visual impairment has been strongly associated with the incidence of dementia. Appropriate cognitive screening for the elderly with visual impairment is crucial for early identification of dementia and its management. Due to challenges in processing visually presented stimuli among participants, the cut-off score of the Hong Kong version of the Montreal Cognitive Assessment for the Visually Impaired (HKMoCA-VI), also known as MoCA-BLIND or MoCA-22, was unknown. Besides, the cognitive status of elderly with visual impairment residing in care homes is rarely investigated. The current study aimed to 1) establish the cut-off score for HKMoCA-VI and 2) examine the general cognitive functioning of elderly with visual impairment living in residential homes in Hong Kong in terms of MoCA-VI percentile scores. METHOD: HKMoCA-VI and the Cantonese version of the Mini-Mental State Examination (CMMSE) were administered to 123 visually impaired elderly residents in care homes in Hong Kong. Percentile scores of HKMoCA-VI by age and education level were determined, and the concurrent validity, sensitivity, and specificity of HKMoCA-VI were assessed. RESULTS: A cut-off score 12 was suggested for HKMoCA-VI, which yielded a sensitivity and specificity of 89.29% and 83.58%, respectively. Moreover, it strongly correlated with CMMSE, indicating satisfactory concurrent validity. CONCLUSIONS: HKMoCA-VI is suggested to be a viable cognitive screening tool for elderly individuals with visual impairment in residential homes. Further modifications to enhance the sensitivity and specificity of the measure are proposed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".