Medical cannabis authorization and risk of emergency department visits and hospitalization due to psychotic disorders: A propensity score-matched cohort study
Bibliographic record
Abstract
Despite evidence showing that recreational cannabis use is associated with a higher risk of psychotic disorders, this risk has not been well characterized for patients using medical cannabis. Therefore, this study assessed the risk of emergency department (ED) visits and hospitalization for psychotic disorders (the study outcome) among adult patients authorized to use medical cannabis. We performed a retrospective cohort study on patients authorized to use medical cannabis in a group of Ontario cannabis clinics between 2014 and 2019. Using clinical and health administrative data, each patient was matched by propensity scores to up to 3 population-based controls. Conditional Cox proportional hazards regressions were used to assess the risk. Among 54,006 cannabis patients matched to 161,265 controls, 39 % were aged ≤50 years, and 54 % were female. Incidence rates for psychotic disorders were 3.00/1000 person-years (95%CI: 2.72-3.32) in the cannabis group and 1.88/1000 person-years (1.75-2.03) in the control group. A significant association was observed, with an adjusted hazard ratio of 1.38 (95%CI: 1.19-1.60) in the total sample and 1.63 (1.40-1.91) in patients without previous psychotic disorders. The results suggest that cannabis authorization should include a benefit-risk assessment of psychotic disorders to minimize the risk of events requiring emergency attention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".