Risk Factors for Subsequent Impaired Driving by Injured Passengers
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to evaluate the rate of, and risk factors for, subsequent impaired driving activity (IDA) in a cohort of injured passengers who were treated for injuries in a Canadian trauma center. METHODS: We studied adult passengers who were occupants in vehicles involved in motor vehicle crashes (MVCs) and either included in the British Columbia (BC) trauma registry (January 1, 1992-December 31, 2004) or treated in the emergency department (ED) of Vancouver General Hospital (VGH; January 1, 1999-December 31, 2003). Passengers were linked to their driver's license and hence to their driving record using personal health number and demographic information. Injured passengers were stratified into 3 groups based on their blood alcohol concentration (BAC) at time of ED presentation: group 1: BAC = 0, group 2: 0 < BAC ≤ 17.3 mM (0.08%), group 3: BAC > 17.3 mM (0.08%). Two outcome variables were studied: involvement in a subsequent IDA and time to their first subsequent IDA. IDA was defined as a criminal code conviction for impaired driving, a 24-h or 90-day license suspension for impaired driving, and/or involvement in an MVC where police cited alcohol as a factor. Time to first IDA following the index event among passenger BAC groups was compared with Kaplan-Meier survival analysis. Cox proportional hazards models were employed to examine the effect of various potential risk factors on time to engage in first IDA. RESULTS: Injured passengers with any BAC at the time of ED visit were more likely to engage in IDA and had their first IDA sooner after the index event than those with zero BAC. Among this cohort of injured passengers, 12.1 percent with BAC = 0, 29.9 percent with 0 < BAC ≤ 17.3 mM (0.08%), and 37.8 percent with a BAC > 17.3 mM (0.08%) engaged in IDA. Compared to passengers with BAC = 0, group 3 passengers and group 2 passengers were 2.06 times and 1.79 times more likely to engage in future IDA. Twenty-five percent of injured passengers engaged their first IDA by 57 and 38 months in groups 2 and 3, respectively. Previous IDA and being male were also significant risk factors for future IDA. Those with a history of IDA before the index event were 2.37 times more likely to engage in subsequent IDA. CONCLUSIONS: Injured alcohol-impaired passengers are at high risk for IDA and should be included in impaired driving prevention programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".