Cannabis, alcohol and other drug findings in fatally injured drivers in Ontario
Bibliographic record
Abstract
The purpose of this study was to determine the prevalence of cannabis, alcohol and other drug use in drivers of motor vehicles who died in crashes in the Canadian province of Ontario from January 2016 through December 2018 along with the characteristics of these drivers and some of the circumstances of the crash in which they were involved. Toxicological tests were performed on blood samples obtained from 921 driver fatalities for whom postmortem blood samples were submitted to the Center of Forensic Sciences for analysis. The results were coded into a database along with basic demographic and crash characteristics and examined for prominent characteristics and patterns. Overall, among the 921 cases examined, 495 (53.7%) tested positive for alcohol, cannabis (tetrahydrocannabinol or THC), or another psychoactive drug. The number of cases that tested positive for THC (251) exceeded the number of cases that tested positive for alcohol (241) as well as the number that tested positive for a drug other than THC (235). In 38% of positive cases, more than one substance was detected. Alcohol and THC were most commonly detected among males; females most frequently tested positive for a drug other than THC, notably medications with depressant effects. Alcohol-involved driver fatalities were most common on weekends and most likely involved single vehicle crashes. Driver fatalities that tested positive for THC or another drug were more evenly distributed throughout the week and were more likely to have been in multi-vehicle crashes. The present study highlights the use of cannabis and other drugs by drivers. The patterns of crashes and the characteristics of drivers involved indicate that the characteristics of driver fatalities involving cannabis and/or other drug use differ from those of alcohol and require new, innovative approaches targeting high-risk times, groups and behaviors. Continued monitoring of the toxicological findings from blood samples obtained from drivers killed in motor vehicle crashes will be a key element in efforts to reduce the impact of drug use by drivers on road safety.
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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 | 0.000 | 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 | 0.003 | 0.001 |
| 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".