Systematic review of the pharmacological treatment of alcohol use disorders in individuals infected with hepatitis C
Bibliographic record
Abstract
Treating alcohol use disorders (AUD) is critical in individuals suffering from hepatitis C infection (HCV). Aside from psychosocial interventions, pharmacological treatment is effective for decreasing alcohol consumption and promoting abstinence. However, unique factors belonging to HCV-infected individuals, such as baseline hepatic vulnerability and possible ongoing hepatitis C treatment, complicate AUD drug therapy. The goal of this review is to systematically identify, summarize, and evaluate the existing evidence on the pharmacological management of AUD in HCV-infected individuals. MEDLINE, Embase, PsycINFO, and the Cochrane Central Register of Controlled Trials were searched for English- and French-language articles published from 1993 to December 2013. The search criteria focused on clinical trials and observational studies assessing the efficacy and/or safety of pharmacological management of AUD in patients infected with HCV. Of 421 identified studies, three were included for analysis. Two were observational studies assessing the safety of disulfiram. One was a randomized controlled trial assessing the efficacy and safety of baclofen. There is paucity of data regarding the efficacy and safety of pharmacological treatment of AUD in HCV-infected individuals, with studies being small series and showing significant heterogeneity. No strong recommendations can be made based on the current studies as to which pharmacological option should be preferred in this sub-population.
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".