Does tobacco dependence worsen cannabis withdrawal in people with and without schizophrenia‐spectrum disorders?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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) | 0.000 | 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 it