Cognitive Screening in Asia: Recognizing the Role of the Patient-Family Unit
Bibliographic record
Abstract
BACKGROUND/AIMS: Cognitive screening programmes may improve awareness and help at-risk subjects receive earlier medical attention. Cognitive profiles of subjects who attend cognitive screening by personal choice (self-referred) compared to those where the referral was initiated by family members (family-referred) were compared. METHODS: A cross-sectional survey of community subjects attending a cognitive screening initiative. Performance on the MMSE, Frontal Assessment Battery (FAB), Elderly Cognitive Assessment Questionnaire (ECAQ) and Even Briefer Assessment Scale for Depression was evaluated. RESULTS: A total of 342 subjects with a mean age of 59.2 +/- 9.0 years were screened. Overrepresentation of Chinese and Indian subjects and underrepresentation of Malay subjects was noted. The prevalence of cognitive impairment ranged from 7.0 to 9.6% depending on the screening instrument used. Of the 342 subjects, 267 were self-referred, while 75 subjects were family-referred. Family-referred subjects had lower MMSE (p < 0.001), lower ECAQ (p < 0.001) and lower FAB (p < 0.001) scores but were not more depressed compared to self-referred subjects (p = 0.904). Only the difference in ECAQ scores remained significant after adjustment for baseline differences in age and education. The prevalence of hypertension, diabetes mellitus and hypercholesterolaemia was not statistically different between the 2 groups. CONCLUSIONS: Family members play a crucial role in the diagnosis of cognitive impairment, especially in older subjects with fewer years of education.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".