Trends in cannabis adverse reaction reports: A descriptive analysis of spontaneous reporting data submitted to the Canada Vigilance Program since legalization and regulation of cannabis for non-medical purposes in Canada
Bibliographic record
Abstract
BACKGROUND: The cannabis control framework implemented by Canada in October 2018 established a robust post-market surveillance system for cannabis products, adopting tools and practices from the existing pharmacovigilance system for pharmaceuticals and health products. The cannabis vigilance system relies on spontaneous reporting of adverse reactions, allowing Health Canada to collect, monitor and assess health effects involving cannabis. In this study, we examine trends in adverse reaction reports involving legal cannabis products since legalization and regulation in Canada. METHODS: Unique case reports of adverse reactions involving cannabis were collected through the Canada Vigilance Program. Case details were extracted from each report involving legal cannabis as a suspected product. Each case was also assessed for causality to determine the likelihood of association between the cannabis product(s) and the reported event(s). The case data was then aggregated and descriptively analyzed to identify adverse reaction case patterns, including the demographic profiles and use patterns of individuals reporting adverse reactions to cannabis. RESULTS: Overall, individuals reporting an adverse reaction to a cannabis product (n = 698) were 56.0 ± 20.0 years of age. 45.4% of reporting individuals were female, and 67.5% of individuals self-reported using cannabis for medical purposes, with pain management as the most cited reason for medical use. Most cases were reported as serious (62.3%), citing "other medically important condition" as the primary reason for seriousness (58.6%), and 68.8% of all cases involved cannabis extracts. Frequently reported events included hallucination, headache, nausea, dizziness and dyspnea. Some events were more frequently reported with products containing a greater concentration of tetrahydrocannabinol to cannabidiol, whereas others were more frequently reported with products containing a greater concentration of cannabidiol to tetrahydrocannabinol. Causality was assessed for 668 events; most were assessed as being "possibly" associated with the reported cannabis product. CONCLUSIONS: The post-market adverse reaction reporting system for cannabis products has provided valuable safety information about cannabis products available for legal retail sale in Canada. The data collected through this framework have helped identify emerging risks associated with legal cannabis products; contributed to international data about cannabis effects and risks; informed the development of communication materials related to new and emerging risks; and provided evidence to inform regulatory decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".