The role of alcohol use in the aetiology and progression of liver disease: A narrative review and a quantification
Bibliographic record
Abstract
ISSUES: Alcohol use has been shown to impact on various forms of liver disease, not restricted to alcoholic liver disease. APPROACH: We developed a conceptual framework based on a narrative review of the literature to identify causal associations between alcohol use and various forms of liver disease including the complex interactions of alcohol with other major risk factors. Based on this framework, we estimate the identified relations for 2017 for the USA. KEY FINDINGS: The following pathways were identified and modelled for the USA for the year 2017. Alcohol use caused 35 200 (95% uncertainty interval 32 800-37 800) incident cases of alcoholic liver cirrhosis. There were 1700 (uncertainty interval 1100-2500) acute hepatitis B and C virus (HBV and HCV) infections attributable to heavy-drinking occasions, and 14 000 (uncertainty interval 5900-19 500) chronic HBV and 1700 (uncertainty interval 700-2400) chronic HCV infections due to heavy alcohol use interfering with spontaneous clearance. Alcohol use and its interactions with other risk factors (HBV, HCV, obesity) led to 54 500 (uncertainty interval 50 900-58 400) new cases of liver cirrhosis. In addition, alcohol use caused 6600 (uncertainty interval 4200-9300) liver cancer deaths and 40 700 (uncertainty interval 36 600-44 600) liver cirrhosis deaths. IMPLICATIONS: Alcohol use causes a substantial number of incident cases and deaths from chronic liver disease, often in interaction with other risk factors. CONCLUSION: This additional disease burden is not reflected in the current alcoholic liver disease categories. Clinical work and prevention policies need to take this into consideration.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".