New Opioid Use and Risk of Emergency Department Visits Related to Motor Vehicle Collisions in Ontario, Canada
Bibliographic record
Abstract
Importance: Opioids can impair motor skills and may affect the ability to drive; however, the association of opioid use with driving ability is not well established. Objective: To examine the risk of motor vehicle collisions (MVCs) among drivers starting opioid therapy compared with that among drivers starting nonsteroidal anti-inflammatory drug (NSAID) therapy. Design, Setting, and Participants: This population-based, retrospective cohort study included all residents of Ontario aged 17 years or older who started new prescription analgesic therapy between March 1, 2008, and March 17, 2019. Exposures: Initiation of opioid therapy or NSAID therapy, ascertained through prescription dispensing records in administrative data. Main Outcomes and Measures: The primary outcome was an emergency department visit for injuries sustained as a driver in an MVC during the 14 days after starting analgesic therapy. Inverse probability treatment weighting was used to balance baseline covariates, and weighted Cox proportional hazards regression models were used to assess the association between new analgesic therapy and hazard of an emergency department visit after an MVC. Results: Of the 1 454 824 individuals included in the study, 765 464 (52.6%) were new opioid recipients and 689 360 (47.4%) were new NSAID recipients. Most participants were aged 65 years or older (75.2%), and 55.2% were women. Of 194 individuals who had emergency department visits for injuries from an MVC within 14 days of initiating therapy, 98 (50.5%) were opioid recipients (3.41 per 1000 person-years; 95% CI, 2.80-4.15 per 1000 person-years) and 96 (49.5%) were NSAID recipients (3.64 per 1000 person-years; 95% CI, 2.98-4.45 per 1000 person-years). There was no significant difference in the risk of an emergency department visit for MVC injuries between opioid and NSAID recipients (weighted hazard ratio, 0.94; 95% CI, 0.70-1.25). Conclusions and Relevance: The findings of this study suggest that the hazard of an emergency department visit for injuries relating to an MVC as a driver is similar between individuals starting prescription opioids and those starting prescription NSAIDs. These results may be useful for patients, clinicians, and caregivers when considering new analgesic therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".