Unintentional cannabis exposures in children pre- and post-legalization: A retrospective review from a Canadian paediatric hospital
Bibliographic record
Abstract
Objectives: Canada legalized recreational cannabis in October 2018. Cannabis is increasingly available in numerous forms-especially edibles-that make children vulnerable to unintentional intoxication. We sought to: determine the frequency of visits due to cannabis intoxication pre- and post-legalization; characterize the clinical features and circumstances of cannabis intoxication in the paediatric population; and create greater awareness among healthcare providers about this issue. Methods: We performed a retrospective chart review of Emergency Department visits at the Children's Hospital of Eastern Ontario (Ottawa, ON) between March 2013 and September 2020. Inclusion criteria were: age <18 years; unintentional cannabis ingestion, identified by ICD-10 codes T40.7 and X42. We assessed basic demographics, clinical signs and symptoms, exposure details, investigations, and patient disposition. Results: A total of 37 patients (22 male) met inclusion criteria, mean age 5.9±3.8 years. Most visits (32; 86%) occurred in the 2-year period after legalization. Altered levels of consciousness, lethargy/somnolence, tachycardia, and vomiting were the most common presenting signs and symptoms. The majority of exposures were to edibles (28; 76%) in the home setting (30; 81%). Poison control and child protective services were involved in 19 (51%) and 22 (59%) of cases, respectively. Twelve patients (32%) required admission to the hospital, the majority of whom stayed <24 h. Conclusions: Our data confirm increased paediatric hospital visits related to unintentional cannabis exposures post-legalization. Consideration of this clinical presentation is critical for acute care providers. Advocacy for safe storage strategies and appropriate enforcement of marketing/packaging legislation are imperative for public health policymakers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".