Prescription medication use as a risk factor for motor vehicle collisions: a responsibility study
Bibliographic record
Abstract
INTRODUCTION: Previous studies on the effect of prescription medications on MVCs are sparse, not readily applicable to real-world driving and/or subject to strong selection bias. This study examines whether the presence of prescription medication in drivers' blood is associated with being responsible for MVC. METHODS: This modified case-control study with responsibility analysis compares MVC responsibility rates among drivers with detectable levels of six classes of prescription medications (anticonvulsants, antidepressants, antihistamines, antipsychotics, benzodiazepines, opioids) versus those without. Data were collected between January 2010 and July 2016 from emergency departments in British Columbia, Canada. Collision responsibility was assessed using a validated and automated scoring of police collision reports. Multivariable logistic regression was used to determine OR of responsibility (analysed in 2018-2019). RESULTS: Unadjusted regression models show a significant association between anticonvulsants (OR 1.92; 95% CI 1.20 to 3.09; p=0.007), antipsychotics (OR 5.00; 95% CI 1.16 to 21.63; p=0.03) and benzodiazepines (OR 2.99; 95% CI 1.56 to 5.75; p=0.001) with collision responsibility. Fully adjusted models show a significant association between benzodiazepines with collision responsibility (aOR 2.29; 95% CI 1.16 to 4.53; p=0.02) after controlling for driver characteristics, blood alcohol and Δ-9-tetrahydrocannabinol concentrations, and the presence of other prescription medications. Antidepressants, antihistamines and opioids exhibited no significant associations. CONCLUSION: There is a moderate increase in the risk of a responsible collision among drivers with detectable levels of benzodiazepines in blood. Physicians and pharmacists should consider collision risk when prescribing or dispensing benzodiazepines. Public education about benzodiazepine use and driving and change to traffic policy and enforcement measures are warranted.
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.
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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".