CANNABIS AND TRAFFIC COLLISIONS: FINDINGS FROM A CASE CROSSOVER STUDY OF PATIENTS PRESENTING TO EMERGENCY DEPARTMENTS IN TWO CANADIAN CITIES
Bibliographic record
Abstract
Background Limited epidemiological data exist on the role of cannabis in traffic collisions, and existing research has employed designs that have been weak in several respects (measurement, control group, confounders). Our study addresses these limitations. Objectives (1) To determine the prevalence of cannabis use in drivers presenting to hospital following a traffic collision; (2) To determine if cannabis use leads to an increased collision risk. Methods Participants were drivers presenting to emergency departments in Toronto and Halifax with an injury due to a traffic collision, between April 2009 and July 2011. Drivers were interviewed about the collision event and substance use. A case-crossover design was employed with a fixed control condition measuring substance use retrospectively for the same comparable time interval in the preceding week. Blood samples were tested for cannabis, alcohol, and other drugs, with examination of active THC metabolites through gas chromatography-mass spectrometry. Results Of 864 eligible driver, self-report and blood samples found that 95 (11%) used cannabis prior to driving. Among those who provided blood, 73 of 368 drivers (19.8%) tested positive for cannabis (+ >0.2 ng/ml active THC metabolites), indicating pre-collision use. Conditional fixed effects models indicate that cannabis use increased the odds of a crash by 5.17 (CI 2.78 to 9.58). Sensitivity analyses were also performed. Significance Nearly one in five collision-involved drivers who provided blood was impaired by cannabis, a higher rate than found in previous studies. Cannabis was also shown to dramatically increase collision risk, and trend that has been confirmed in recent mFeta analyses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".