Surveillance from the high ground: sentinel surveillance of injuries and poisonings associated with cannabis
Bibliographic record
Abstract
INTRODUCTION: In October 2018, Canada legalized the nonmedical use of cannabis for adults. The aim of our study was to present a more recent temporal pattern of cannabis-related injuries and poisonings found in the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) database and provide a descriptive summary of the injury characteristics of cannabis-related cases captured in a nine-year period. METHODS: We conducted a search for cannabis-related cases in the eCHIRPP database reported between April 2011 and August 2019. The study population consisted of patients between the ages of 0 and 79 years presenting to the 19 selected emergency departments across Canada participating in the eCHIRPP program. We calculated descriptive estimates examining the intentionality, external cause, type and severity of cannabis-related cases to better understand the contextual factors of such cases. We also conducted time trend analyses using Joinpoint software establishing the directionality of cannabis-related cases over the years among both children and adults. RESULTS: Between 1 April 2011, and 9 August, 2019, there were 2823 cannabis-related cases reported in eCHIRPP, representing 252.3 cases/100 000 eCHIRPP cases. Of the 2823 cannabis-related cases, a majority involved cannabis use in combination with one or more substances (63.1%; 1780 cases). There were 885 (31.3%) cases that involved only cannabis, and 158 cases (5.6%) that related to cannabis edibles. The leading external cause of injury among children and adults was poisoning. A large proportion of cannabis-related cases were unintentional in nature, and time trend analyses revealed that cannabis-related cases have recently been increasing among both children and adults. Overall, 15.1% of cases involved serious injuries requiring admission to hospital. CONCLUSION: Cannabis-related cases in the eCHIRPP database are relatively rate, a finding that may point to the fact that mental and behavioural disorders resulting from cannabis exposure are not generally captured in this surveillance system and the limited number of sites found across Canada. With Canada's recent amendments to cannabis regulations, ongoing surveillance of the health impacts of cannabis will be imperative to help advance evidence to protect the health of Canadians.
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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 | 0.002 | 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 | 0.000 |
| 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".