Setting the baseline: a description of cannabis poisonings at a Canadian pediatric hospital prior to the legalization of recreational cannabis
Bibliographic record
Abstract
INTRODUCTION: This study describes the events and circumstances preceding children aged 16 years or younger being treated for cannabis poisoining in the emergency department (ED) of a Canadian pediatric hospital. METHODS: We extracted cannabis poisoning treated in the ED at British Columbia Children's Hospital (BCCH) between 1 January, 2016 and 31 December, 2018, from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) database. The poisonings were distinguished by the inadvertent or intentional ingestion of cannabis. We reviewed the hospital's electronic health information system and the patients' health records to obtain additional information on the context, including spatial and temporal characteristics. RESULTS: Of the 911 poisonings treated at BCCH, 114 were related to intentional cannabis use (12.5%). Fewer than 10 poisonings resulted from inadvertent ingestions by children and the median age for these was 3 years. All inadvertent ingestion occurred at home and involved cannabis belonging to the patient's family. The vast majority of poisonings resulted from the intentional use of cannabis only (28.9%) or cannabis use with other psychoactive substances (co-ingestions; 71.1%). The median patient age was 15 years. Most patients reported consuming cannabis through inhalation with peers. Cannabis and co-ingestion poisonings were more often reported on weekdays than weekends. The consumption of cannabis leading to poisoning more often occurred in private residences. Patients with cannabis poisoning more often sought medical treatment themselves or were helped by their family. CONCLUSION: The characteristics of cannabis poisonings among children are described for the three-year period prior to recreational cannabis legalization in Canada in order to set a baseline for future comparisons. Implications for improving injury prevention initiatives and policies are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".