Incidence and Predictors of Cannabis-Related Poisoning and Mental and Behavioral Disorders Among Patients with Medical Cannabis Authorization: A Cohort Study
Bibliographic record
Abstract
Abstract Objective: As medical cannabis use increases in North America, establishing the safety profile of cannabis is a priority. The objective of this study was to assess rates of emergency department (ED) visits and hospitalizations due to poisoning by cannabis and cannabis-related mental health disorders among medically authorized cannabis patients in Ontario, Canada, between 2014 and 2017.Methods: This is a retrospective longitudinal study conducted among a cohort of patients who received an authorization to use cannabis for treating various health conditions in Ontario, Canada. The cannabis cohort was selected using data collected in participating Canadian cannabis clinics. Outcomes included: ED visit/hospitalization with a main diagnosis code for cannabis/cannabinoid poisoning; and ED visit/hospitalization with a main diagnosis code for mental/behavioural disorders due to cannabis use. The Spearman correlation and univariate Cox proportional hazard regression was utilized.Results: From 29153 patients who received medical authorization, 23091 satisfied the inclusion criteria. During a median follow-up of 240 days, 14 patients visited the ED or were hospitalized for cannabis poisoning – with an incidence rate of 8.06 per 10,000 person-years patients (95%CI: 4.8-13.6). A total of 26 patients visited the ED or were hospitalized for mental and behavioural disorders due to cannabis use- with an incidence rate of 15.0 per 10,000 person-years (95%CI: 10.2-22.0). Predictors of cannabis-related mental and behavioural disorders include prior substance use disorders (drugs and alcohol), other mental disorders, age, diabetes, and chronic obstructive pulmonary disease. Conclusions: The results suggest that the incidence of cannabis poisoning or cannabis-related mental and behavioural disorders was low among patients who were authorized to use cannabis to treat a health condition. Identified predictors can help to target patients with potential risk of the studied outcomes.
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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 | 0.000 |
| 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 | 0.001 | 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".