Development and validation of a new cognitive screening test: The Hong Kong Brief Cognitive Test (HKBC)
Bibliographic record
Abstract
OBJECTIVES: To develop and examine the validity of a new brief cognitive test with less educational bias for screening cognitive impairment. METHODS: A new cognitive test, Hong Kong Brief Cognitive Test (HKBC), was developed based on review of the literature, as well as the views of an expert panel. Three groups of subjects aged 65 or above were recruited after written consent: normal older people recruited in elderly centres, people with mild NCD (neurocognitive disorder), and people with major NCD. The brief cognitive test, Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA), were administered to the subjects. The performance of HKBC in differentiating subjects with major NCD, mild NCD, and normal older people were compared with the clinical diagnosis, as well as the MMSE and MoCA scores. RESULTS: In total, 359 subjects were recruited, with 99 normal controls, 132 subjects with major NCD, and 128 with mild NCD. The mean MMSE, MoCA, and HKBC scores showed significant differences among the 3 groups of subjects. In the receiving operating characteristic curve analysis of the HKBC in differentiating normal subjects from those with cognitive impairment (mild NCD + major NCD), the area under the curve was 0.955 with an optimal cut-off score of 21/22. The performances of MMSE and MoCA in differentiating normal from cognitively impaired subjects are slightly inferior to the HKBC. CONCLUSIONS: The HKBC is a brief instrument useful for screening cognitive impairment in older adults and is also useful in populations with low educational level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".