Medical Cannabis Authorization and the Risk of Cardiovascular Events: A Longitudinal Cohort Study
Bibliographic record
Abstract
Abstract Background Despite the rising rates of legalization of cannabis worldwide, very few investigators have studied the effects of medical cannabis use on healthcare utilization and subsequent cardiovascular (CV) risk. This study assesses the risk of CV-related emergency department (ED) visit/hospitalization in adult patients authorized to use medical cannabis in Ontario, Canada from 2014–2017. Methods This is a longitudinal cohort study of patients who received medical cannabis authorization and followed-up in cannabis clinics, matched to population-based controls. The primary outcome was an ED visit/hospitalization for any CV event; and secondary outcome was for acute coronary syndrome (ACS)/stroke. Conditional Cox proportional hazards regression was used to assess the association between cannabis use and risk. Results 18,653 cannabis patients were matched to 5,1243 controls. Incidence rates for any CV event were 28.34/1000 person-years and 19/1000 person-years in the cannabis and controls group, respectively- (aHR) of 1.52 (95%CI: 1.31–1.77). The aHR among patients with a history of CV event and those without were 1.50 (1.28–1.75) and 1.26 (1.03–1.55), respectively. The aHR among males and females were 1.52 (1.24–1.87) and 1.41 (1.11–1.80). For the ACS and stroke, the aHR was 1.44 (95%CI: 1.08–1.93). When stratified by sex, only men had an increased risk of ACS and stroke; aHR 1.77 (1.23–2.56). Conclusions Medical cannabis authorization was associated with an increased risk of ED visits or hospitalization for CV events including stroke and ACS - among males and for patients with a prior CV condition.
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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 | 0.000 |
| 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 | 0.000 |
| 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".