Hong Kong Brief Cognitive Test for identifying symptomatic Alzheimer's disease and other types of dementia
Bibliographic record
Abstract
BackgroundThe Hong Kong Brief Cognitive Test (HKBC) has demonstrated high discriminative ability for patients with cognitive impairment in both Cantonese- and Mandarin-speaking populations.ObjectiveTo evaluate the diagnostic efficacy of the HKBC in identifying dementia and mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and other common types of dementia.MethodsSixty-one patients with dementia due to AD, 30 patients with MCI due to AD, 47 patients with subcortical ischemic vascular dementia (SIVD), 50 patients with frontotemporal lobar degeneration (FTLD), 17 patients with Lewy body dementia (LBD), and 37 cognitively unimpaired controls (CUCs) were recruited and completed the HKBC, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The diagnostic performance of each test was analyzed via receiver operating characteristic curve analysis. Impairment in cognitive domains on the HKBC was analyzed in patients with symptomatic AD.ResultsScores of the HKBC, MMSE and MoCA were significantly lower in patients with all types of dementia, AD (dementia and MCI), and non-AD dementia (SIVD, FTLD, and LBD) than in CUCs. The most appropriate cutoff scores of the HKBC were 24 for identifying AD and LBD, 22 for identifying SIVD and FTLD, and 26 for identifying MCI due to AD from CUCs. HKBC memory and language scores were significantly lower in patients with MCI due to AD than in CUCs.ConclusionsThis study demonstrated that the HKBC could efficiently identify patients with common types of dementia and was sensitive in screening early AD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".