Betel Nut Usage Is a Major Risk Factor for Coronary Artery Disease
Bibliographic record
Abstract
AIM: The objective of our study was to assess betel nut usage as one of the major risk factors associated with coronary artery disease. METHODS: This case control study consisted of 300 controls and 300 cases. A structured questionnaire was administered to the participants to assess consumption of betel nut and confounding variables. A respondent was considered a regular consumer of betel nut if he/she consumed one or more pieces of betel nut every day for a period of greater than 6 months. RESULTS: About 8 in 10 betel nut chewers developed coronary artery disease. After adjusting for diabetes and hypertension, the odds ratio analysis depicted 7.72 times greater likelihood for coronary artery disease in patients who chewed betel nut for more than 10 years. CONCLUSION: Our study concludes that betel nut chewing is a significant risk factor leading to the development of coronary artery disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".