Influence of cannabis and alcohol on motor vehicle injury severity in Canadian trauma centres: a prospective study
Bibliographic record
Abstract
BACKGROUND: Alcohol and delta-9-tetrahydrocannabinol (THC) (main impairing ingredient of cannabis) are both crash contributors that interfere with motor vehicle operation. However, the relationship between drug concentration and crash injury severity is unclear for either drug. We aim to clarify the relationship between blood alcohol concentration (BAC) and crash injury severity, based on healthcare system utilisation, with and without THC. METHODS: The National Drug Driving Study is an ongoing prospective study involving 17 Canadian trauma centres. Eligible subjects included drivers aged 16+ who visited a participating trauma centre and had blood drawn as part of routine care within 6 hours of a crash. Deidentified blood samples were tested for alcohol and THC using gas chromatography-flame ionisation detection and liquid chromatography/tandem mass spectrometry. Study outcomes included admission to hospital and admitted patients' length of hospital stay. RESULTS: 10 322 injured drivers visited a participating trauma centre between 2018 and 2023. 1649 (16.0%), 1716 (16.6%) and 463 (4.5%) drivers had detectable levels of alcohol, THC or both, respectively. Compared with sober drivers (BAC=0), drivers with 0%<BAC<0.08% had increased odds of admission (aOR=1.69, 95% CI=1.31 to 2.19), as did drivers with BAC≥0.08% (aOR=1.36, 95% CI=1.16 to 1.60). THC did not modify the relationship between alcohol and admission. Neither alcohol nor THC predicted were associated with length of stay following admission. INTERPRETATION: Alcohol increases hospital admissions after crashes but does not have a dose-response relationship with admission or length of stay. THC does not moderate this relationship.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".