Is There a Safe Alcohol Consumption Limit for the General Population and in Patients with Liver Disease?
Bibliographic record
Abstract
Excessive alcohol consumption represents an important burden for health systems worldwide and is a major cause of liver- and cancer-related deaths. Alcohol consumption is mostly assessed by self-report that often underestimates the amount of drinking. While alcohol use disorders identification test - version C is the most widely used test for alcohol use screening, in patients with liver disease the use of alcohol biomarker could help an objective assessment. The amount of alcohol that leads to significant liver disease depends on gender, genetic background, and coexistence of comorbidities (i.e., metabolic syndrome factors). All patients with alcohol-associated liver disease are recommended to follow complete abstinence and they should be treated within multidisciplinary teams. Abstinence slows down and even reverses the progression of liver fibrosis and can help recompensate patients with complicated cirrhosis. Whether there is a safe amount of alcohol in the general population is a matter of intense debate. Large epidemiological studies showed that the safe amount of alcohol to avoid overall health-related risks is lower than expected even in the general population. Even one drink per day can increase cancer-related death. In patients with any kind of chronic liver disease, especially in those with metabolic-associated steatotic liver disease, no alcohol intake is recommended. This review article discusses the current evidence supporting the deleterious effects of small-to-moderate amounts of alcohol in the general population and in patients with underlying chronic liver disease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".