The influence of acutely administered nicotine on cue-induced craving for gambling in at-risk video lottery terminal gamblers who smoke
Bibliographic record
Abstract
Evidence indicates that tobacco use and gambling often co-occur. Despite this association, little is known about how tobacco use affects the propensity to gamble. Nicotine, the putative addictive component of tobacco, has been reported to potentiate the hedonic value of other nonsmoking stimuli. Environmental cues have been identified as an important contributor to relapse in addictive behavior; however, the extent to which nicotine can affect the strength of gambling cues remains unknown. This study examined whether nicotine influences subjective ratings for gambling following gambling cues. In a mixed within/between-subjects design, 30 (20 men) video lottery terminal (VLT) gamblers ('moderate-risk' or 'problem' gamblers) who smoke daily were assigned to nicotine (4 mg deliverable) or placebo lozenge conditions. Subjective and behavioral responses were assessed at baseline, following lozenge, following neutral cues, and following presentation of gambling cues. Nicotine lozenge was found to significantly reduce tobacco-related cravings (P<0.05) but did not affect gambling-related cravings, the choice to play a VLT, or other subjective responses. These results suggest that a low dose of acutely administered nicotine does not increase cue-induced craving for gambling in at-risk VLT gamblers who smoke.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".