The Impact of Benzodiazepines on Safe Driving
Bibliographic record
Abstract
OBJECTIVE: Benzodiazepines are prescribed to relieve anxiety and aid sleep. Studies demonstrate that benzodiazepines increase odds of crash involvement, but little evidence exists regarding their impact on crash responsibility. We examined the impact of benzodiazepines on crash responsibility by drug half-life and driver age, using a case-control design with drivers aged 20 and over involved in fatal crashes in the United States from 1993-2006. METHODS: Drivers (all with BAC = 0) were classified as having no benzodiazepines detected versus short, intermediate, or long half-life benzodiazepines. Cases were drivers with at least one potentially unsafe driving action (UDA) in relation to the crash (e.g., speeding), a proxy measure for crash responsibility; controls had no UDAs recorded. Odds ratios (ORs) of any UDA by benzodiazepines half-life exposure were calculated, with adjustment for age, sex, other medication usage, and prior driving record. RESULTS: Compared with drivers not using benzodiazepines, drivers taking intermediate or long half-life benzodiazepines demonstrated increased odds of an UDA from ages 25 (intermediate OR: 1.59; 95% CI = 1.08, 2.33; long OR: 1.68; 95% CI = 1.34, 2.12) to 55 (intermediate OR: 1.50; 95% CI = 1.09, 2.06; long OR: 1.33; 95% CI = 1.12, 1.57). Drivers taking short half-life benzodiazepines did not demonstrate increased odds compared to drivers not using benzodiazepines. CONCLUSIONS: Given the potential impact of benzodiazepines on driver safety, further experimental research is needed to better understand the effect of benzodiazepines on crash responsibility.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".