Cardiovascular and Cerebrovascular Disease Incidence Among 42785 Adults: The Thai Cohort Study, 2005-2013
Bibliographic record
Abstract
BACKGROUND: Due to economic and social development in Thailand, cardiovascular disease (CVD) and cerebrovascular disease (CVA) have been gradually replacing infectious diseases and have become the main threat to health in this country. METHOD: This study used the 2005 baseline data of 42785 members of the Thai cohort study (TCS) to identify health risk factors correlated with incidence of CVD and/or CVA over 8 years (2005- 2013). We applied multivariate logistic regression to investigate associations between demographic and socioeconomic factors, health conditions, and personal lifestyle factors and CVD and/or CVA incidence. RESULTS: The cumulative incidence of CVD and/or CVA in males was more than three times that in females. CVD and/or CVA incidence was correlated with ageing, obesity (AOR: 1.67, 95% CI: 1.16-2.40) and previous diagnosis with diabetes (AOR: 3.09, 95% CI: 1.80-5.30), hyperlipidaemia (AOR: 1.54, 95% CI: 1.08-2.19), hypertension (AOR: 1.71, 95% CI: 1.13-2.59), chronic kidney disease (AOR: 2.35, 95% CI: 1.35-4.10), and depression/anxiety (AOR: 2.76, 95% CI: 1.64-4.63). Short sleep time was positively associated with CVD and/or CVA in the Thai Cohort Study. An inverse association between performing housework and the incidence of CVD and/or CVA was also identified. However, current smoking had a significant positive correlation with the incidence of CVD and/or CVA for participants. CONCLUSION: Older age, obesity, underlying diseases, short sleep time, and current smoking were the risk factors for CVD and/or CVA incidence for the participants. However, housework, as an incidental exercise, could protect people against the risk of CVD and/or CVA.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".