Excessive Alcohol Consumption and Hypertension: Clinical Implications of Current Research
Bibliographic record
Abstract
Substantial evidence demonstrates that: 1) heavy alcohol consumption (three or more standard drinks per day) is associated with and predictive of hypertension; 2) reduction in alcohol consumption is associated with a significant dose-dependent lowering of mean systolic and diastolic blood pressure; and 3) physician advice can reduce heavy drinking in hypertensive patients. These findings suggest that the routine evaluation of alcohol consumption in hypertensive patients is warranted. The Alcohol Use Disorders Identification Test-C (AUDIT-C), a brief, three-question screening test, is useful in this regard. Alcohol biomarkers can also play a role in detecting and monitoring heavy drinking in hypertensive patients whose self-reports on the AUDIT-C are suspect. Carbohydrate-deficient transferrin, a new alcohol biomarker with high specificity, can provide objective data for feedback and counseling. A routine search for excessive use of alcohol, along with brief interventions and monitoring, can have a major impact on reducing the prevalence of hypertension in the general population.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".