Cannabis and Tobacco Co-Use Predicts Psychosis in Clinical High Risk Cohorts
Bibliographic record
Abstract
Abstract Cannabis and tobacco use are highly prevalent among people with psychosis and are associated with medical comorbidities and poor prognosis. Concurrent use of cannabis and tobacco (‘co-use’) is rising in the general population, but has not been studied in psychosis. Given the devastating consequences of cannabis and tobacco use, it is critical to understand how their co-use affects psychiatric symptoms and the development of psychosis. Here we used the North American Prodrome Longitudinal Study-2, a multisite prospective study of individuals at clinical high risk for psychosis (CHR), and healthy controls to examine baseline differences in psychiatric symptoms and conversion to psychosis across substance groups: (1) CHR tobacco use, (2) CHR cannabis use, (3) CHR co-use, (4) CHR non-tobacco or cannabis use, (5) CHR without substance use and (6) healthy controls. Among 1,012 participants (734 CHR, 278 controls), more frequent cannabis and tobacco use was linked to greater psychiatric symptom severity. In survival analyses, heavy cannabis and light tobacco co-use (HR = 2.93, 95% confidence interval (CI) [1.23–6.97], P = 0.015) was associated with higher risk of conversion than no use of either substance. Co-use of tobacco and cannabis was not associated with psychiatric symptom severity but did predict higher risk of conversion to psychosis. These results highlight the need for strategies that address co-use in CHR populations to mitigate potential long-term psychiatric consequences.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".