Alcohol confounds relationship between cannabis misuse and psychosis conversion in a high‐risk sample
Bibliographic record
Abstract
OBJECTIVE: Cannabis use has been examined as a predictor of psychosis in clinical high-risk (CHR) samples, but little is known about the impact of other substances on this relationship. METHOD: Substance use was assessed in a large sample of CHR participants (N = 370, mean age = 18.3) enrolled in the multisite North American Prodrome Longitudinal Study Phase 1 project. Three hundred and forty-one participants with cannabis use data were divided into groups: No Use (NU, N = 211); Cannabis Use without impairment (CU, N = 63); Cannabis Abuse/Dependence (CA/CD, N = 67). Participants (N = 283) were followed for ≥2 years to determine psychosis conversion. RESULTS: Alcohol (45.3%) and cannabis (38.1%) were the most common substances. Cannabis use groups did not differ on baseline attenuated positive symptoms. Seventy-nine of 283 participants with cannabis and follow-up data converted to psychosis. Survival analysis revealed significant differences between conversion rates in the CA/CD group compared with the No Use (P = 0.031) and CU group (P = 0.027). CA/CD also significantly predicted psychosis in a regression analysis, but adjusting for alcohol use weakened this relationship. CONCLUSION: The cannabis misuse and psychosis association was confounded by alcohol use. Non-impairing cannabis use was not related to psychosis. Results highlight the need to control for other substance use, so as to not overstate the cannabis/psychosis connection.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".