Health-Risk Factors and the Prevalence of Chronic Kidney Disease: Cross-Sectional Findings from a National Cohort of 87 143 Thai Open University Students
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is becoming a major health challenge worldwide as its aetiology has transferred from predominantly infectious disease to emerging chronic diseases, especially diabetes and hypertension. A rapid health-risk transition driven by economic development is transforming Thailand which is now becoming an ageing country where chronic diseases are a major health burden. METHODS: This study used the 2005 baseline cross-sectional dataset of 87,143 Thai Cohort Study members to investigate risk factors associated with CKD. Using multivariate logistic regression, we looked into the relationship between CKD and demographic and socioeconomic factors, personal health status and various health-related behaviours. RESULTS: The prevalence of CKD in men was lower than that in women (2.5% vs 2.7%). In both sexes, CKD is associated with ageing, cigarette smoking and drinking alcohol, having diabetes, high lipids and hypertension. In men, CKD was associated with living in rural areas, having a low income, a higher BMI, short sleeping and having Western fast food. In women, marriage is associated with a higher risk of CKD. CONCLUSIONS: CKD is strongly associated with ageing, underlying diseases, smoking and drinking. Hypertension, elevated lipids, or diabetes are all risk factors that could be prevented or detected and treated. The Ministry of Public Health should encourage Thai people to consume healthy food, maintain a normal weight, stop smoking and drink alcohol in moderation, all of which will help prevent CKD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".