Low-dose diazepam primes motivation for alcohol and alcohol-related semantic networks in problem drinkers
Bibliographic record
Abstract
Considerable research with animals indicates that the GABA-benzodiazepine (BZ) system plays a key role in alcohol reinforcement. However, only limited research appears to have assessed this issue directly in humans. The present study investigated whether low-dose diazepam would cross-prime motivation for alcohol in problem drinkers. Twelve male problem drinkers (Alcohol Dependence Scale; ADS score > or =9) received oral diazepam (5 mg) and placebo, in a counterbalanced manner on separate sessions. There were three measures of primed motivation for alcohol: self-reported desire for alcohol, consumption of placebo beer in an ostensible taste test procedure, and automatically executed vocal reading responses to Alcohol versus Neutral words on a computer-based task. Diazepam significantly increased beer consumption, and produced a marginally significant increase in reported desire for alcohol. On the reading task, diazepam significantly decreased response latency to Alcohol words relative to Neutral words. Latency to Alcohol words correlated significantly with beer consumption under the drug. Moreover, response latency to Alcohol words under the drug also predicted ADS scores. Thus, severity of dependence was directly linked with vulnerability to a BZ priming effect on motivation for alcohol. These findings provide direct evidence that the GABA-BZ system plays an important role in alcohol reinforcement in problem drinkers.
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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".