Emergency Department Pediatric Visits in Alberta for Cannabis After Legalization
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Canada legalized nonmedical cannabis possession and sale in October 2018. In the United States, state legalization has been tied to an increase in cannabis-related emergency department (ED) visits; however, little research exists on provincial changes in pediatric visits after nationwide legislation. We compared pre- and postlegalization trends in pediatric cannabis-related ED visits and presentation patterns in urban Alberta EDs. METHODS: Retrospective National Ambulatory Care Reporting System data were queried for urban Alberta cannabis-related ED visits among patients aged <18 years from October 1, 2013, to February 29, 2020. Population subgroups included children (aged 0-11 years), younger adolescents (12 to 14 years), and older adolescents (15 to 17 years). We calculated interrupted time series, incident rate ratios (IRRs), and relative risk (RR) ratios to identify trend change. IRRs identified changes against growth-adjusted Alberta population, while RRs measured presentation pattern changes against prelegalization ED visits. RESULTS: Pediatric visit volume did not change postlegalization when accounting for preexisting volume trends. Unintentional ingestions increased in children (IRR: 1.77, 95% confidence interval [CI]: 1.42 to 2.20 and RR: 1.24, 95% CI: 1.05 to 1.47, respectively) and older adolescents (IRR: 1.36, 95% CI: 1.07 to 1.71 and RR: 1.48, 95% CI: 1.21 to 1.81, respectively). Presentation patterns remained similar, although older adolescent co-ingestant use decreased (RR: 0.77, 95% CI: 0.67 to 0.88), whereas hyperemesis cases increased (RR: 1.64, 95% CI: 1.13 to 2.37). CONCLUSIONS: Cannabis legalization has increased child and older adolescent unintentional cannabis ingestions, increasing child cannabis-related ED visits. Changes highlight need for public health interventions targeting pediatric exposures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".