Global burden of alcoholic liver diseases
Bibliographic record
Abstract
Liver diseases contribute markedly to the global burden of mortality and disease. This paper provides an overview from a global perspective of the contribution of alcohol to liver diseases. The Global Burden of Disease study methodology was used to estimate the burden of alcohol-attributable liver cirrhosis and alcohol-attributable liver cancer in 2010 as measured by deaths and disability adjusted life years (DALYs). This methodology estimates attributable fractions based on alcohol exposure distribution and relative risks associated with different levels of drinking. Globally, in 2010, alcohol-attributable liver cirrhosis was responsible for 493,300 deaths (156,900 female deaths and 336,400 male deaths) and 14,544,000 DALYs (4,112,000 DALYs for women and 10,432,000 DALYs for men), representing 0.9% (0.7% for women and 1.2% for men) of all global deaths and 0.6% (0.4% for women and 0.8% for men) of all global DALYs, and 47.9% of all liver cirrhosis deaths (46.5% for women and 48.5% for men) and 46.9% of all liver cirrhosis DALYs (44.5% for women and 47.9% for men). Alcohol-attributable liver cancer was responsible for 80,600 deaths (14,800 female deaths and 65,900 male deaths) and 2,142,000 DALYs (335,000 DALYs for women and 1,807,000 DALYs for men). The burden of alcohol-attributable liver cirrhosis and liver cancer is high and entirely preventable. Interventions to reduce alcohol consumption are recommended as a population health priority and may range from taxation increases for alcoholic beverages to increases in screening and treatment rates for alcohol use disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".