Alcohol as a Risk Factor for Global Burden of Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it