Alcohol as a Risk Factor for Global Burden of Disease
Bibliographic record
Abstract
AIM: To make quantitative estimates of the burden of disease attributable to alcohol in the year 2000 on a global basis. DESIGN: Secondary data analysis. MEASUREMENTS: Two dimensions of alcohol exposure were included: average volume of alcohol consumption and patterns of drinking. There were also two main outcome measures: mortality, i.e. the number of deaths, and disability-adjusted life years (DALYs), i.e. the number of years of life lost to premature mortality or to disability. All estimates were prepared separately by sex, age group and WHO region. FINDINGS: Alcohol causes a considerable disease burden: 3.2% of the global deaths and 4.0% of the global DALYs in the year 2000 could be attributed to this exposure. There were marked differences by sex and region for both outcomes. In addition, there were differences by disease category and type of outcome; in particular, unintentional injuries contributed most to alcohol-attributable mortality burden while neuropsychiatric diseases contributed most to alcohol-attributable disease burden. DISCUSSION/CONCLUSIONS: The underlying assumptions are discussed and reasons are given as to why the estimates should still be considered conservative despite the considerable burden attributable to alcohol globally.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".