Smoking, Hypertension, Alcohol Consumption, and Risk of Abdominal Aortic Aneurysm in Men
Bibliographic record
Abstract
Despite the known protective association between moderate alcohol consumption and ischemic heart disease, little is known about the effects of alcohol consumption on abdominal aortic aneurysms (AAA). The authors analyzed prospective, biennially updated data for a cohort of 39,352 US men from 1986 to 2002. The association of incident AAA diagnosis with alcohol consumption in grams per day was assessed at baseline and by using alcohol consumption data updated every 4 years, controlling for previously reported cardiovascular risk factors. During 576,374 person-years of follow-up, 376 newly diagnosed cases of AAA were demonstrated. After adjustment for other risk factors for AAA, including smoking, hypertension, and body mass index, alcohol consumption at baseline was independently associated with AAA diagnosis (p for trend = 0.03), with a maximum hazard ratio of 1.21 (95% confidence interval: 0.78, 1.87) for > or =30.0 g (approximately > or =2 standard drinks) of daily alcohol consumption. This association was stronger when the updated alcohol consumption data were assessed rather than simply baseline exposure (p for trend = 0.02); the hazard ratio for the highest level of intake (> or =30.0 g/day) was 1.65 (95% confidence interval: 1.03, 2.64). Small numbers limited analyses by beverage type, but liquor demonstrated the strongest positive association with AAA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".