Opioids and the Risk of Motor Vehicle Collision: A Systematic Review
Bibliographic record
Abstract
Aim and Background: Opioid analgesics are amongst the most commonly prescribed medications, but questions remain regarding their impact on the day-to-day functioning of patients including driving. Previous studies of the effects of opioids on the risk of motor vehicle collisions (MVCs) have shown inconsistent findings. We set out to perform a systematic review on the risk of MVC associated with prescription opioid exposure. Methods: We searched Medline, PubMed, EMBASE, Scopus and TRID from January 1990 to January 31, 2020 for primary studies assessing prescribed opioid use and MVCs. Studies that used surrogate driving capability tests were excluded. The Newcastle-Ottawa scale was used to assess quality of included studies. Findings: We identified 12 observational studies that met inclusion criteria. Among those, 7 studies found an increased risk of MVC among those participants who had a concomitant opioid prescription at the time of the MVC and 2 found no significant increase of culpability of fatal MVC. The three studies that evaluated the presence of a dose-response relationship between the dose of opioids taken and the effects on MVC risk reported the existence of a dose-response relationship. Due to the heterogeneity of the different studies, a quantitative meta-analysis to sum evidence was deemed unfeasible. Interpretation: Our review indicates that opioid prescriptions are likely associated with an increased risk of MVCs. Further studies are warranted to strengthen this finding, and investigate additional factors such as individual opioid medications, opioid doses and dose adjustments, and opioid tolerance for their effect on MVC risk. Funding: This project was supported by the Rady Faculty of Health Sciences, University of Manitoba and the Chronic Disease Innovation Centre, Seven Oaks Hospital. Declaration of Interest: None to declare.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".