Prevalence of Mild Cognitive Impairment in Rural Thai Older People, Associated Risk Factors and their Cognitive Characteristics
Bibliographic record
Abstract
INTRODUCTION: Mild cognitive impairment (MCI) is a transitional stage between normal cognition and dementia. A review showed that 10-15% of those with MCI annually progressed to Alzheimer's disease. OBJECTIVE: This study aimed to investigate the prevalence and risk factors associated with MCI as well as the characteristics of cognitive deficits among older people in rural Thailand. METHODS: A cross-sectional study in 482 people who were 60 years old and over was conducted in northern Thailand. The assessments were administered by trained occupational therapists using demographic and health characteristics, Mental Status Examination Thai 10, Activities of Daily Living - Thai Assessment Scale, 15-item Geriatric Depression Scale and the Montreal Cognitive Assessment-Basic (MoCA-B, Thai version). RESULTS: The mean age of MCI was 68.3 ± 6.82 years, and most had an education ≤4 years. The prevalence of MCI in older people was 71.4% (344 out of 482), and it increased with age. Low education and diabetes mellitus (DM) were the significant risk factors associated with cognitive decline. Older people with MCI were more likely to have an education ≤4 years (RR 1.74, 95% CI 1.21-2.51) and DM (RR 1.19, 95% CI 1.04-1.36) than those who did not. The 3 most common cognitive impairments according to MoCA-B were executive function (86%), alternating attention (33.1%) and delayed recall (31.1%). CONCLUSION: The prevalence of MCI in older Thai people in a rural area is high compared with that in other countries. The explanation might be due to low education and underlying disease associated with MCI. A suitable program that can reduce the prospects of MCI in rural Thailand is needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".