Opioid Dose and Risk of Road Trauma in Canada
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Use of opioids may predispose drivers to road trauma, yet the effect of opioid dose on this association is unknown. METHODS: We conducted a population-based nested case-control study of patients aged 18 to 64 years who received at least 1 publicly funded prescription for an opioid from April 1, 2003, through March 31, 2011. Cases were defined as having an emergency department visit related to road trauma. Patients without road trauma served as a control group matched to cases by age, sex, index year, prior road trauma, and a disease risk index. We compared the risk of road trauma among patients treated with doses of opioids ranging from very low to very high (<20 to ≥200 morphine equivalents daily). In a subgroup analysis, we stratified our analysis by driver status. RESULTS: Among 549 878 eligible adults, we identified 5300 cases with road trauma and matched an equal number of controls. Multivariate adjustment yielded no significant association between escalating opioid dose and odds of road trauma (adjusted odds ratio ranged between 1.00 and 1.09). However, a significant association between opioid dose and road trauma was observed among drivers. Compared with very low opioid doses, drivers prescribed low doses had a 21% increased odds of road trauma (adjusted odds ratio, 1.21 [95% CI, 1.02-1.42]); those prescribed moderate doses, 29% increased odds (1.29 [1.06-1.57]); those prescribed high doses, 42% increased odds (1.42 [1.15-1.76]); and those prescribed very high doses, 23% increased odds (1.23 [1.02-1.49]). CONCLUSIONS: Among drivers prescribed opioids, a significant relationship exists between drug dose and risk of road trauma. This association is distinct and does not appear with passengers, pedestrians, and others injured in road trauma.
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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