Intravenous Alcohol Administration Selectively Decreases Rate of Change in Elasticity of Demand in Individuals With Alcohol Use Disorder
Bibliographic record
Abstract
BACKGROUND: Alcohol demand is a key behavioral economic concept that provides an index of alcohol's relative reinforcing value. Initial studies have reported that alcohol demand increases during alcohol administration and in response to alcohol cues. However, the extent to which these effects are observed explicitly in samples composed of individuals with alcohol use disorder (AUD) and are operative in conjunction with each other has not been studied. METHODS: To address this gap in the literature, we assessed alcohol demand during an alcohol challenge and subsequent alcohol cue-exposure paradigm in non-treatment-seeking, alcohol-dependent (i.e., DSM-IV criteria) participants (N = 27). Specifically, participants completed 2 counterbalanced intravenous, placebo-controlled, alcohol administration sessions followed by a controlled cue-exposure paradigm. At baseline and at breath alcohol concentration of 0.06 g/dl, participants completed the alcohol purchase task, assessing estimated alcohol consumption at escalating prices. Participants were also assessed for alcohol demand following each cue exposure. RESULTS: During alcohol administration, there was a significant decrease in the rate of change in elasticity compared with placebo, and during the cue-reactivity paradigm, there was a significant main effect such that alcohol cues decreased the rate of change in elasticity relative to water cues. There were no statistically significant differences in other demand indices. CONCLUSIONS: These findings provide further evidence that alcohol administration increases price insensitivity and extends the literature on alcohol's effects on demand by using a clinical sample with AUD and by adding a placebo-alcohol condition.
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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".