A multi-site study examining the tobacco withdrawal trajectory in people with tobacco and cannabis co-use
Bibliographic record
Abstract
BACKGROUND: Approximately 30 % of people who use tobacco also use cannabis, and rates of co-use are rising. Relative to people who use tobacco alone (TO), individuals who co-use tobacco and cannabis (TC) experience greater difficulty with tobacco cessation, yet mechanisms underlying this phenomenon remain unexplored. Leveraging data from a multi-site, double-blind clinical trial for tobacco cessation, we compared the trajectory of tobacco withdrawal, a strong predictor of relapse, between TC and TO during 11-weeks of tobacco treatment. METHODS: People seeking treatment for tobacco were randomized to one of three arms (placebo, nicotine patch or varenicline) and followed for 11-weeks. Participants were parsed according to their cannabis use status determined by a cannabis-positive urine toxicology at screen (N = 1246). We selected participants with end-of-treatment biochemically verified 7-day point prevalence tobacco abstinence (N = 330; TC, n = 55 and TO, n = 275) and examined group differences in tobacco withdrawal severity using the Minnesota Nicotine Withdrawal Scale (MNWS) at baseline, week 1, 4, 8, and week 11 (end-of-treatment). RESULTS: Controlling for age, treatment arm, and site, we found a significant interaction (group x time) effect for withdrawal severity (p < 0.01). Bonferroni-corrected post-hoc comparisons revealed that relative to TO, TC had elevated withdrawal scores at week 1 (TC, M=9.3 ± 5.5; TO, M=7.1 ± 5.6; p < 0.01); no other timepoints showed between-group differences. CONCLUSIONS: People who co-use experience greater tobacco withdrawal severity one-week post abstinence compared to people who only use tobacco. Personalized interventions that target immediate tobacco withdrawal and/or cannabis use may help improve tobacco cessation rates for people who co-use both substances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".