Health-Risk Factors and 8-Year Incidence of Kidney Disease in Transitional Thailand: Prospective Findings From a Large National Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Kidney disease (KD) is increasing its burden in Thailand but prospective observational KD studies are few. So we analysed 8-year nationwide Thai Cohort Study (TCS) data on KD incidence, distribution and risk association among Thais.DESIGN & METHOD: TCS is a longitudinal study of the Thai health-risk transition among Open University student residing nationwide. At baseline (2005) the cohort members analysed here were aged 15-88 years and did not have KD. At the follow up in 2013 (n=41638) incident KD was reported based on doctor diagnosis. We analysed the 8-year cumulative incidence of KD and its association with risk factors by using multivariable logistic regression.RESULTS: The incidence of KD (2005 to 2013) was 4.0%; the rate in men (5.9) was significantly higher than in women (2.5). KD increased significantly for both increasing age and body mass index (BMI) (p trend <0.001 for both). Its incidence was strongly associated with concurrent diseases including hypertension, diabetes and high blood lipids and moderately associated with increased frequency of cigarette smoking, instant food, roast or smoked food and soft drink consumption. KD decreased with increases in personal income, household assets, walking and physical activity.CONCLUSION: Physical activity, high income and household assets prevented KD. Lifestyle changes such as smoking and high consumption of instant, roast or smoked food and soft drink increased risk of KD. Government should encourage more physical activity and less smoking, salt and sugar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".