Examining the relationships between cognitive function and physical and mental health: interim findings from HKMMSOP
Bibliographic record
Abstract
Abstract Background Population ageing is associated with an increase in the number of persons with dementia, which constitutes a significant public health issue. The Hong Kong Mental Morbidity Survey for Older People (HKMMSOP) was carried out to evaluate the local prevalence of dementia and the factors that modulated the disease development. Method Participants aged 60 or over, stratified with age groups, were recruited through random sampling of residential addresses in Hong Kong. They underwent an assessment of their cognitive function (Montreal Cognitive Assessment, MoCA; Clinical Dementia Rating, CDR), physical health (Cumulative Illness Rating Scale, CIRS) and mental health (Clinical Interview Schedule‐Revised, CIS‐R; Short Warwick‐Edinburgh Mental Wellbeing Scale, SWEMWBS). Associative factors of neurocognitive disorders were determined by multinomial regression, with neurocognitive disorders as dependent variables. Result In this interim analysis, 4369 participants of HKMMSOP were included. Their mean age was 69.6, and there was a slight female preponderance (56.3%). On average, they received 8.8 years of education, and their mean MoCA score was 23.8 (SD 4.9). Using CDR, 71.8% were evaluated as having normal cognition, 23.1% with a mild neurocognitive disorder (ND), and 5.2% with a major ND. Older or less educated participants had a higher risk of developing mild and major ND (p<0.001). But the relationships between cognition and physical and mental health differed in the mild and major ND groups. Among those with mild ND, a higher CIRS total score (p<0.001), a higher CIS‐R total score (p<0.001) and a lower SWEMWBS score (p = 0.002) were significantly associated with CDR. However, only CIRS total score (p = 0.004) and SWEMWBS score (p<0.001) remained significant in the major ND group. Conclusion Poorer physical and mental health and lower mental well‐being and life satisfaction were associated with an increased risk of mild ND. But as the disease progressed, lower mental well‐being and life satisfaction and poorer physical health remained significant factors. On the contrary, a higher level of mental symptoms, as exemplified by CIS‐R, was not significantly associated with major ND. It sheds some light on the factors that may modulate the course of the disease and appropriate preventive measures.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".