Effects of naltrexone on alcohol self‐administration and craving: meta‐analysis of human laboratory studies
Bibliographic record
Abstract
Randomized clinical trials have established the efficacy of naltrexone for reducing quantity of alcohol consumption and incidence of relapse to heavy drinking. To evaluate putative treatment mechanisms, human laboratory studies have examined naltrexone's effects on alcohol responses and self-administration during short-term medication protocols. Results from these studies are inconsistent and have yet to be examined in aggregate. This meta-analysis aimed to quantify naltrexone's effects on alcohol self-administration and craving in the context of placebo-controlled human laboratory trials. Potential moderators of medication effects were also examined. Meta-analyses of alcohol self-administration (k = 9, N = 490) and craving (k = 16, N = 748) confirmed that, under controlled experimental conditions, naltrexone reduces the quantity of consumption (Hedges' g = -.277, SE = .074, 95 percent CI = -.421, -.133, p < .001) and magnitude of self-reported craving (g = -.286, SE = .066, 95 percent CI = -.416, -.156, p < .001) relative to placebo. Subgroup and moderation analyses found no evidence that effect sizes differed by study population (dependent versus non-dependent drinkers), laboratory paradigm or duration of medication exposure. These results substantiate prior evidence for reductions in event-level craving and consumption as potential treatment mediators, also establishing effect sizes to inform future human laboratory trials. From a clinical perspective, these results may provide additional evidence regarding naltrexone's efficacy in the context of acute or subacute dosing regimens.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".