The effects of recreational cannabis legalization in Alberta on poison control centre calls and paediatric emergency department visits
Bibliographic record
Abstract
Objectives: To characterize cannabis-related presentations to the two major paediatric emergency departments (EDs) in Alberta as well as calls to Alberta's Poison and Drug Information Services (PADIS) and detect any changes in relation to legalization. Methods: This was a retrospective medical record review analyzing all paediatric (ages 0 to 18) ED presentations for cannabis-related concerns. The two sites included were the Stollery Children's Hospital in Edmonton and the Alberta Children's Hospital in Calgary. We searched the PADIS database for all calls in the province for 'Cannabinoids and Analogues' for ages 0 to 19. The rates prior to and after legalization were compared. Results: While we saw no overall difference in ED visits, pre- and post-legislation we found an increase in unintentional overdoses in children under 12 years of age (7% versus 15%, proportion change 1.13). The severity of presentations did not change during this time period (37% versus 42%, P 0.254). We also found an increase in calls to PADIS in the 2 years after legalization. There was an increase in exposure to edible cannabis formulations during this time period. Conclusion: This study combines a province-wide medical record review of ED visits with poison control centre information to provide a complete look at cannabis intoxication in paediatric patients over the time of legalization. It adds to the growing body of evidence that legalization of recreational cannabis, especially edible formulations has resulted in increased unintentional overdoses in young children.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".