The Effect of Naltrexone on Alcohol’s Stimulant Properties and Self‐Administration Behavior in Social Drinkers: Influence of Gender and Genotype
Bibliographic record
Abstract
BACKGROUND: Few pharmacological treatments for alcohol dependence are available. Moreover, the best supported treatment, naltrexone hydrochloride, appears to work for only some. METHODS: To investigate potential predictors of these differential responses, 40 social drinkers (20 women) were administered 6 days of treatment with naltrexone vs. placebo in a double-blind, counterbalanced, crossover design. At the end of each treatment period, participants received a single dose of their preferred alcoholic beverage followed by the opportunity to work for additional alcohol units using a progressive ratio (PR) breakpoint paradigm. All subjects but one were genotyped for the A118G polymorphism of the mu opioid receptor gene (OPRM1). RESULTS: Naltrexone decreased the ethanol-induced 'euphoria' to a priming dose of alcohol in two subgroups: (i) in women, and (ii) in subjects with the A118G polymorphism of the mu opioid receptor gene (OPRM1). Naltrexone did not decrease motivation to work for additional alcoholic beverages on the PR task regardless of gender or genotype. CONCLUSIONS: The results add to the evidence that naltrexone decreases positive subjective effects of alcohol, with preferential effects in distinct subgroups. Similar effects in heavier drinkers might decrease alcohol use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".