Cause-specific mortality risk in alcohol use disorder treatment patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorders (AUD) are highly disabling. Recent studies reported much higher relative risks for all-cause mortality in AUD patients compared with earlier studies. Systematic evidence regarding cause-specific mortality among AUD patients has been unavailable to date. METHODS: Studies were identified through MEDLINE, EMBASE and Web of Science up to August 2012. Following MOOSE guidelines, prospective and historical cohort studies assessing cause-specific mortality risk from AUD patients at baseline compared with the general population were selected. Data on several study characteristics, including AUD assessment, follow-up period, setting, location and cause-specific mortality risk compared with the general population were abstracted. Random-effect meta-analyses were conducted. RESULTS: Overall, 17 observational studies with 6420 observed deaths among 28 087 AUD patients were included. Pooled standardized mortality ratios (SMRs) after 10 years of follow-up among men were 14.8 (95% confidence interval: 8.7-24.9) for liver cirrhosis, 18.0 (11.2-30.3) for mental disorders, 6.6 (5.0-8.8) for death by injury and around 2 for cancer and cardiovascular diseases. SMRs were substantially higher in women, with fewer studies available. For many outcomes the risk has been increasing substantially over time. CONCLUSIONS: Cause-specific mortality among AUD patients was high in all major categories compared with the general population. There has been a lack of recent research, and future studies should focus on the influence of comorbidities on excess mortality risk among AUD patients. Efforts to reduce these risks should be a priority, given that successful treatment reduces mortality risk substantially for a relatively common psychiatric disease.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".