Cannabis cravings predict cigarette use in schizophrenia: a secondary analysis from two cannabis abstinence studies
Bibliographic record
Abstract
Background: People with schizophrenia have high rates of cannabis use disorder (CUD) as well as co-use of tobacco. Tobacco may be used to alleviate cannabis cravings.Objectives: We conducted a secondary analysis of 7 and 28 days of cannabis abstinence from two datasets of participants with CUD with and without schizophrenia to assess cannabis craving, cannabis use in grams per day (GPD) and cigarettes per day (CPD).Methods: We recruited 49 participants (schizophrenia n = 29; control n = 20; 2% female) with CUD. All participants received weekly behavioral support sessions to assist with cannabis abstinence/reduction. GPD and CPD were assessed with the 7-day Timeline Follow-back; the Marijuana Craving Questionnaire (MCQ) assessed compulsivity, emotionality, expectancy and purposefulness subscales. Effects of GPD and MCQ subscales on CPD at days 7 and 28 were assessed with analysis of covariance.Results: At day seven, participants with schizophrenia scored higher on compulsivity (t = 2.72, df = 46.8, p < .01), emotionality (t = 2.48, df = 45.2, p = .02), and purposefulness (t = 2.32, df = 39.41, p = .02) craving subscales than control participants. There was a significant interaction between high purposefulness craving and low GPD on high CPD at day seven (F1,23 = 4.76, p = .039) and day 28 (F1,13 = 7.02 p = .02) in schizophrenia participants but not control participants.Conclusion: We found preliminary evidence that due to cannabis craving intensity, people with schizophrenia and CUD may use more cigarettes when reducing cannabis use, as compared to CUD participants without schizophrenia. Strategies to manage cannabis craving and avoid increasing tobacco use will be useful to implement in intervention strategies.Clinical Trial Name Effects of Repetitive Transcranial Magnetic Stimulation (rTMS) on Cannabis Use and Cognitive Outcomes in SchizophreniaURL: www.clinicaltrials.gov; Registration Number: NCT03189810;
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".