Proportion of Injured Drivers Presenting to a Tertiary Care Emergency Department Who Engage in Future Impaired Driving Activities
Bibliographic record
Abstract
OBJECTIVE: We determined the rate of, and predictive factors for, subsequent impaired driving activity (IDA) by injured drivers treated in a Canadian tertiary care emergency department (ED) following a motor vehicle crash (MVC). METHODS: We retrospectively identified all drivers injured in a MVC who presented to our tertiary care, urban ED (1999-2003) and had their blood alcohol content (BAC) measured. Injured drivers were categorized by BAC: group 1, BAC = 0; group 2, 0 < BAC < or = 17.3 mM (80 mg/dL, legal limit); and group 3, BAC > 17.3 mM. IDA was defined as any of the following: a conviction for impaired driving; a 24-h or 90-day license suspension for impaired driving; involvement in alcohol-related MVC. Time to IDA following the index event between groups was compared with Kaplan-Meier survival analyses. Effects of covariates on time to IDA were analyzed using Cox proportional hazards models. RESULTS: During the study period, 1489 injured drivers met study criteria: 1171 in group 1, 51 in group 2, and 267 in group 3. During an average follow-up of 52.4 months, 82 (30.7%) group 3 drivers engaged in subsequent IDA, compared with 80 (6.8%) group 1 drivers (p < 0.0001). Youth, male gender, history of previous IDA, and the number of previous IDA events were all associated with a significant increase in subsequent IDA. A history of IDA was the strongest predictor of future IDA in group 1 (440% increase risk) and in group 3 (80% increased risk). The magnitude of BAC elevation above the legal limit was not predictive of future IDA. CONCLUSIONS: A high portion of injured impaired drivers who present to hospital engage in repeat IDA following discharge. Besides impairment at time of hospital visit, the best predictor of future IDA is a history of IDA prior to the index event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".