Global Burden of Alcohol Use Disorders and Alcohol Liver Disease
Bibliographic record
Abstract
Alcohol use is a major risk factor for burden of mortality and morbidity. Alcoholic liver disease (ALD) and alcohol use disorders (AUDs) are important disease outcomes caused by alcohol use. We will describe the global mortality and burden of disease in disability-adjusted life years for ALD and AUDs, based on data from the comparative risk assessment of the World Health Organization for 2016. AUDs have a limited impact on mortality in this assessment, since alcohol poisonings are almost the only disease category directly attributable to AUDs; most other alcohol-related deaths are indirect, and the cause which directly led to the death, such as liver cirrhosis, is the one recorded on the death certificate. Burden of disease for AUDs is thus mainly due to disability resulting from alcohol use. In contrast to AUDs, ALD is one of the major lethal outcomes of alcohol use, and burden of disease is mainly due to (premature) years of life lost. Many of the negative outcomes attributable to both AUDs and ALD are due to their interactions with other factors, most notably economic wealth. To avoid alcohol-attributable morbidity and mortality, measures should be taken to reduce the AUDs and ALD burden globally, especially among the poor.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".