The Impact of Benzodiazepines on Safe Driving
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it