Extra‐hepatic morbidity and mortality in alcohol‐related liver disease: Systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Alcohol use increases the risk of many conditions in addition to liver disease; patients with alcohol-related liver disease (ALD) are therefore at risk from both extra-hepatic and hepatic disease. AIMS: This review synthesises information about non-liver-related mortality in persons with ALD. METHODS: A systematic literature review was performed to identify studies describing non-liver outcomes in ALD. Information about overall non-liver mortality was extracted from included studies and sub-categorised into major causes: cardiovascular disease (CVD), non-liver cancer and infection. Single-proportion meta-analysis was done to calculate incidence rates (events/1000 patient-years) and relative risks (RR) compared with control populations. RESULTS: Thirty-seven studies describing 50 302 individuals with 155 820 patient-years of follow-up were included. Diabetes, CVD and obesity were highly prevalent amongst included patients (5.4%, 10.4% and 20.8% respectively). Outcomes varied across the spectrum of ALD: in alcohol-related fatty liver the rate of non-liver mortality was 43.4/1000 patient-years, whereas in alcoholic hepatitis the rate of non-liver mortality was 22.5/1000 patient-years. The risk of all studied outcomes was higher in ALD compared with control populations: The RR of death from CVD was 2.4 (1.6-3.8), from non-hepatic cancer 2.2 (1.6-2.9) and from infection 8.2 (4.7-14.3). CONCLUSION: Persons with ALD are at high risk of death from non-liver causes such as cardiovascular disease and non-hepatic cancer.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".