Dual Cannabis-Methamphetamine Use Doubles Bipolar Disorder Risk After Psychosis: A 25-Year Inpatient Cohort Study from Iran
Bibliographic record
Abstract
Abstract Objective This study aimed to estimate transition rates from substance-induced psychosis (SIP) to schizophrenia or bipolar disorder, evaluate the risk associated with dual use of methamphetamine and cannabis, and identify predictors of chronicity among Iranian men. Methods A 25-year retrospective cohort study (1999–2024) was conducted using clinical data from Shafa Hospital, a tertiary psychiatric referral center in Guilan Province, Iran. The study included 258 male inpatients aged ≥17 years with SIP attributed to cannabis and/or methamphetamine use. Patients with a history of schizophrenia or bipolar disorder were excluded. Outcomes were validated using DSM-V criteria, and Kaplan-Meier survival curves and Cox proportional hazards models were employed for analysis. Results Over a median follow-up of 33 months, 37.6% of participants transitioned to schizophrenia (25.2%) or bipolar disorder (12.4%). The median time to conversion was shorter for bipolar disorder (13.4 months) compared to schizophrenia (26.6 months). Dual cannabis-methamphetamine use significantly increased the risk of bipolar disorder (p=0.008). Familial psychiatric history doubled the risk of schizophrenia (HR=4.29, 95% CI: 2.48–7.41) and tripled the risk of bipolar disorder (HR=3.89, 95% CI: 1.87–8.11). Recurrent hospitalizations were associated with increased risks for both schizophrenia (HR=1.34) and bipolar disorder (HR=2.64). The cumulative incidence of schizophrenia rose linearly to 67.6% at 10 years. Conclusion SIP poses a significant risk for the development of schizophrenia or bipolar disorder, particularly in cases involving dual-substance use, younger age, familial psychiatric history, and frequent hospitalizations. The findings underscore the need for targeted surveillance, early intervention, and long-term, risk-stratified care models in regions with rising methamphetamine use to reduce SIP-related morbidity
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".