Concussions and risk of a subsequent traffic crash: retrospective cohort analysis in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Concussion is an acute injury that may contribute to short-term limitations and potential long-term risks. OBJECTIVE: To test whether a past concussion is associated with the risk of a subsequent serious motor vehicle crash. DESIGN: Population-based longitudinal cohort analysis. SETTING: Ontario, Canada, from 1 April 2002 to 31 March 2022 (178 emergency departments). PATIENTS: Adults diagnosed with a concussion (cases) or an acute ankle sprain (controls), excluding individuals with a disqualifying illness (blindness, dementia, delirium), severe cases resulting in hospitalisation or those who died within 90 days. PRIMARY MEASURE: Subsequent motor vehicle crash requiring emergency medical care. RESULTS: We identified 3 037 028 patients, including 425 158 with a concussion and 2 611 870 with an ankle sprain. A total of 200 603 patients were injured in a subsequent motor vehicle crash over a median follow-up of 10 years, equal to an absolute risk of 1 in 15 patients (6.64 per 1000 patient-years). Patients with a concussion had a 49% higher motor vehicle crash risk compared with those with ankle sprain (adjusted relative risk=1.49, 95% CI 1.47 to 1.50, p<0.001). The increased risk was particularly high in the early weeks after a concussion, remained independent of other observed risk factors, applied to diverse clinical groups and was further accentuated after repeated concussions. The risk extended across a spectrum of crash severity, was accentuated for single-vehicle events, replicated in analyses with artificial intelligence methods adjusting for confounding and remained distinct from the risks of other unrelated medical emergencies. CONCLUSIONS: This study suggests a significant increased risk of a motor vehicle crash after a concussion that may justify a safety warning from clinicians.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".