Additive roles of tobacco and cannabis co-use in relation to delay discounting in a sample of heavy drinkers
Bibliographic record
Abstract
RATIONALE: Alcohol use disorder (AUD) is associated with steeper delay discounting rates; however, it is unknown whether substance co-use, particularly cannabis use, has an additive effect on discounting rates among heavy drinkers. Furthermore, it is unclear whether substance co-use and delay discounting are independently associated with AUD severity. OBJECTIVES: The purpose of this study was to determine whether alcohol, tobacco, and cannabis co-use impacts delay discounting rates. We also sought to determine whether substance co-use and delay discounting were associated with AUD symptom counts. METHODS: The study sample was culled from several human laboratory studies and consisted of 483 heavy drinking individuals who completed a baseline visit (prior to experimental procedures). Participants were divided into groups based on self-reported alcohol, tobacco, and cannabis use during the past 30 days: alcohol only (n = 184), alcohol + cigarettes (n = 89), alcohol + cannabis (n = 82), and tri-use (n = 128). We examined discounting rates across the 4 groups and used multiple linear regression to test whether co-use and delay discounting were associated with AUD symptoms. RESULTS: After adjusting for covariates, individuals in the alcohol + cannabis group and the tri-use group had steeper discounting rates relative to the alcohol-only group. In addition, tri-use and delay discounting rates were independently correlated with a greater number of AUD symptoms. CONCLUSIONS: Delay discounting rates were significantly greater among subgroups reporting cannabis use providing partial support for an additive effect, while also highlighting the importance of co-use substance type. Both tri-use and delay discounting were associated with greater AUD severity, which may provide relevant intervention targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".