Alcohol-induced hypertension: an important healthcare target in Belgium
Bibliographic record
Abstract
Excessive alcohol intake is one of the leading causes of premature death in Europe and particularly in Belgium. Belgian people are consuming more alcohol per year than the European average. It is well established that excessive alcohol consumption is a significant predictor of the development of hypertension (HTN). Two million adults in Belgium suffer from HTN and this number will increase to three million by 2025. Less than 50% of Belgian people treated for HTN are well-controlled. Alcohol reduction in patients with HTN can significantly lower systolic and diastolic blood pressure. After reviewing the epidemiology of HTN and alcohol disorders in Belgium, this paper will focus on the rationale for alcohol screening and brief intervention in primary care. It will also describe the barriers to alcohol screening, and what could be the benefits of alcohol screening for our healthcare system. The authors believe that early identification through alcohol screening and brief intervention in general practice can help to improve the management of patients with HTN, to reach the targets of the WHO Global Action Plan, i.e., a 25% relative reduction in the risk of premature mortality from cardiovascular diseases, cancer, diabetes or chronic respiratory diseases. They are also convinced that this would allow achieving major healthcare savings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".