Substance Use Disorders and Psychoactive Drug Poisoning in Medically Authorized Cannabis Patients: Longitudinal Cohort Study
Bibliographic record
Abstract
Objectives Poisoning from psychoactive drugs and substance use disorders (SUD) have been reported among non-medical cannabis users. However, little is known about medical cannabis users and their risk for poisoning and/or development of SUD. This study assessed the risk of emergency department (ED) visits or hospitalization for 1) poisoning by psychoactive drugs and 2) mental/behavioural disorders due to the use of psychoactive drugs and other substances, in medically authorized cannabis patients in Ontario, Canada from 2014–2017. Methods A cohort study of adult patients authorized for medical cannabis that were matched to population-based controls. ED visit/hospitalization were assessed with a main diagnostic code for: 1) poisoning by psychoactive drugs; 2) mental and behavioural disorder due to psychoactive drugs or other substance use. Conditional Cox proportional hazards regressions were conducted. Results 18,653 cannabis patients were matched to 51,243 controls. During a median follow-up of 243 days, the incidence rate for poisoning was 4.71 per 1,000 person-years (95%CI: 3.71–5.99) for cases and 1.73 per 1,000 person-years (95% CI: 1.36–2.19) for controls. The adjusted hazard ratio (aHR) was 2.45 (95%CI: 1.56–3.84). For mental/behavioural disorders, the incident rates were 8.89 (95% CI: 7.47–10.57) and 5.01 (95% CI: 4.36–5.76) in the cannabis and the controls group. The aHR was 2.27 (95%CI: 1.66–3.11). No difference was observed between males and females ( P-value for interaction > 0.05). Conclusions Our study observed a short-term increased risk of ED visit/hospitalization for poisoning or for mental/behavioural disorders (from use of psychoactive drugs and other substances)- in medically authorized cannabis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".