The lows of getting high: sentinel surveillance of injuries associated with cannabis and other substance use
Bibliographic record
Abstract
OBJECTIVES: Cannabis is a widely used illicit substance that has been associated with acute injuries. This study seeks to provide near real-time injury estimates related to cannabis and other substance use from the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) database. METHODS: Data from the eCHIRPP database, years 2011 to 2016, were analyzed via data mining, descriptive, logistic regression, and sensitivity analyses. Drug use trends over time for cannabis and/or other substances (alcohol, illicit drugs, and medications) were assessed. Descriptive statistics (intent, external cause, and nature of injury) and proportionate injury ratios (PIR) associated with cannabis use are presented. RESULTS: Cannabis use was observed in 184 cases/100,000 eCHIRPP cases, and related injuries were mostly identified as unintentional (66.8%). Poisoning (68.5%) and intoxication (69.4%) were the external cause and nature of injury most associated with these events, and hospitalization was recorded for 14.3% of cases. Per 100,000 eCHIRPP cases, cannabis was used alone in 72.4 cases, and in combination with alcohol, illicit drugs, or medications in 74.6 cases, 11.3 cases, and 7.9 cases, respectively. Relative to non-use, the PIR of hospitalization was not significant for cannabis-only users of either sex (males: PIR 1.0, 95% CI 0.6-1.7, females: PIR 0.9, 95% CI: 0.5-1.7). CONCLUSION: Cannabis use injuries are rare, but can occur when cannabis is used with or without other substances. As Canada considers legislative changes, our finding of cases related to unintentional injury, poisoning, and intoxication suggests areas that might benefit from health literacy efforts.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".