Does tobacco dependence worsen cannabis withdrawal in people with and without schizophrenia‐spectrum disorders?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Rates of cannabis use disorder (CUD) are higher in people with schizophrenia than in the general population. Irrespective of psychiatric diagnosis, tobacco co-use is prevalent in those with CUD and leads to poor cannabis cessation outcomes. The cannabis withdrawal syndrome is well-established and increases cannabis relapse risk. We investigated whether cannabis withdrawal severity differed as a function of high versus no/low tobacco dependence and psychiatric diagnosis in individuals with CUD. METHOD: Men with CUD (N = 55) were parsed into four groups according to schizophrenia diagnosis and tobacco dependence severity using the Fagerstrom Test for Nicotine Dependence (FTND): men with schizophrenia with high tobacco dependence (SCT+, n = 13; FTND ≥ 5) and no/low tobacco dependence (SCT-, n = 22; FTND ≤ 4), and nonpsychiatric controls with high (CCT+, n = 7; FTND ≥ 5) and no/low (CCT-, n = 13; FTND ≤ 4) tobacco dependence. Participants completed the Marijuana Withdrawal Checklist following 12-h of cannabis abstinence. RESULTS: There was a significant main effect of tobacco dependence on cannabis withdrawal severity (p < .001). Individuals with high tobacco dependence had significantly greater cannabis withdrawal severity (M = 13.85 [6.8]) compared to individuals with no/low tobacco dependence (M = 6.49, [4.9]). Psychiatric diagnosis and the interaction effects were not significant. Lastly, cannabis withdrawal severity positively correlated with FTND (r = .41, p = .002). CONCLUSION AND SCIENTIFIC SIGNIFICANCE: Among individuals with CUD and high tobacco dependence, cannabis withdrawal severity was elevated twofold, irrespective of diagnosis, relative to individuals with CUD and no/low tobacco dependence. Findings from this study emphasize the importance of addressing tobacco co-use when treating CUD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".