Alcohol and hypertension: gender differences in dose–response relationships determined through systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: To analyze the dose-response relationship between average daily alcohol consumption and the risk of hypertension via systematic review and meta-analysis. DESIGN: A computer-assisted search was completed for 10 databases, followed by hand searches of relevant articles. Only studies with longitudinal design, quantitative measurement of alcohol consumption and biological measurement of outcome were included. Dose-response relationships were assessed by determining the best-fitting model via first- and second-degree fractional polynomials. Various tests for heterogeneity and publication bias were conducted. FINDINGS: A total of 12 cohort studies were identified from the literature from the United States, Japan and Korea. A linear dose-response relationship with a relative risk of 1.57 at 50 g pure alcohol per day and 2.47 at 100 g per day was seen for men. Among women, the meta-analysis indicated a more modest protective effect than reported previously: a significant protective effect was reported for consumption at or below about 5 g per day, after which a linear dose-response relationship was found with a relative risk of 1.81 at 50 g per day and of 2.81 at an average daily consumption of 100 g pure alcohol per day. Among men, Asian populations had higher risks than non-Asian populations. CONCLUSIONS: The risk for hypertension increases linearly with alcohol consumption, so limiting alcohol intake should be advised for both men and women.
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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 | 0.023 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".