Retrospective Review of Unintentional Pediatric Cannabis Poisonings in Saskatchewan after Federal Legalization
Bibliographic record
Abstract
Background: With the legalization of cannabis in Canada, safety concerns for children should be considered. Despite packaging and dose regulations for edibles and inhaled cannabis, unintentional poisonings are a clinical risk, and its impact on pediatric healthcare resources have not been clearly delineated. Methods: This retrospective cross-sectional chart review evaluated all patients < 19 years presenting to Saskatchewan’s only pediatric trauma center between January 1st, 2020 to June 30th, 2021 with unintentional poisoning. Cannabis and non-cannabis unintentional poisonings were compared using difference of squares and Fisher’s exact test. Results: There were fifty-two unintentional poisonings during the study period, with a mean age of 2.45 years (SD 2.11). Thirty one percent (n=16) were cannabis related, with edibles accounting for at least 63% (n=10) of those admissions. More than 40 percent were transferred from rural communities with an average transport distance of 160 kilometers. Over 18% (n=3) percent were admitted to PICU with no requirement for intubation or vasoactive medications. Conclusions: Since legalization, one third of Saskatchewan’s pediatric unintentional poisonings were due to cannabis, largely from edible ingestions. An increased public awareness and federal government initiatives may attenuate the risk of these ingestions.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".