Alcohol use disorders and mortality: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: To conduct a systematic review and meta-analysis on all-cause mortality in people with alcohol use disorders. METHODS: Using the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines, studies were identified through MEDLINE, EMBASE, and Web of Science up to August, 2012. Prospective and historical cohort studies including a comparison of alcohol use disorder with a control group investigating all-cause mortality risk were included. RESULTS: This meta-analysis included 81 observational studies with 221 683 observed deaths among 853 722 people with alcohol use disorder. In men, the relative risk (RR) among clinical samples was 3.38 (95% confidence interval [CI]: 2.98-3.84); in women it was 4.57 (95% CI: 3.86-5.42). Alcohol use disorders identified in general population surveys showed a twofold higher risk compared with no alcohol use disorder in men; no data were available for women. RRs were markedly higher for those ≤40 years old (ninefold in men, 13-fold in women) while still being at least twofold among those aged 60 years or older. CONCLUSIONS: Mortality in people with alcohol use disorders is markedly higher than thought previously. Women have generally higher mortality risks than men. Among all people with alcohol use disorders, people in younger age groups and people in treatment show substantially higher mortality risk than others in that group.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".