Alcohol Use Disorder Pharmacotherapy in Patients With Alcohol‐Related Liver Disease: A Scoping Review
Bibliographic record
Abstract
Introduction: Alcohol‐associated liver disease (ALD) is one of the most common causes of cirrhosis. Pharmacotherapy for alcohol use disorder (AUD) can improve abstinence rates in patients with cirrhosis, however, there is limited data on how these therapies affect liver‐related outcomes. Methods: A scoping review was completed using multiple electronic search databases. Articles exploring pharmacotherapy for AUD and outcomes for ALD were included. The primary outcome of this study was liver outcomes after receiving pharmacotherapy for AUD, including decompensated cirrhosis, mortality, progression of ALD, and need for liver transplantation. Results: A total of 2521 studies were screened and 3 were selected. A total of 45,948 patients were included, 43,863 (98%) of patients were male, and the mean age was 58.7. Only 2299 (5%) of patients received AUD pharmacotherapy. Receipt of AUD pharmacotherapy was found to be associated with decreased hepatic decompensation and mortality in 2 out of 3 studies. Conclusion: There are limited studies that explore AUD pharmacotherapy and ALD outcomes. Medications AUD may improve hepatic outcomes; however, further prospective studies need to be completed to explore this association.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".