Driving under the influence: a multi-center evaluation of vehicular crashes in the era of cannabis legalization
Bibliographic record
Abstract
BACKGROUND: Eleven states have instituted laws allowing recreational cannabis use leading to growing public health concerns surrounding the effects of cannabis intoxication on driving safety. We hypothesized that after the 2016 legalization of cannabis in California, the use among vehicular injury patients would increase and be associated with increased injury severity. METHODS: San Diego County's five adult trauma center registries in were queried from January 2010 to June 2018 for motor vehicle or motorcycle crash patients with completed toxicology screens. Patients were stratified as toxicology negative (TOX-), positive for only THC (THC+), only blood alcohol >0.08% (ETOH+), THC+ETOH, or THC+ with any combination with methamphetamine or cocaine (M/C). County medical examiner data were reviewed to characterize THC use in those with deaths at the scene of injury. RESULTS: Of the 11,491 patients identified, there were 61.6% TOX-, 11.7% THC+, 13.7% ETOH+, 5.0% THC+ETOH, and 7.9% M/C. THC+ increased from 7.3% to 14.8% over the study period and peaked at 14.9% post-legalization in 2017. Compared with TOX- patients, THC+ patients were more likely to be male and younger. THC+ patients were also less likely to wear seatbelts (8.5% vs 14.3%, p<0.001) and had increased mean Injury Severity Score (8.4±9.4 vs 9.0±9.9, p<0.001) when compared with TOX- patients. There was no difference in in-hospital mortality between groups. From the medical examiner data of the 777 deaths on scene, 27% were THC+. DISCUSSION: THC+ toxicology screens in vehicular injury patients peaked after the 2016 legalization of cannabis. Public education on the risks of driving under the influence of cannabis should be a component of injury prevention initiatives. LEVEL OF EVIDENCE: III, Prognostic.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".