Increased Risk of Traffic Injury After a Cerebrovascular Event
Bibliographic record
Abstract
Background and Purpose- We aimed to determine the long-term risks of a motor vehicle collision after a cerebrovascular event and whether the risks were similar after left- or right-hemispheric events. Methods- We used a population-based registry to identify patients diagnosed with a transient ischemic attack or stroke (hemorrhagic or ischemic) between 2003 and 2013 in Ontario, Canada. Hemispheric laterality was determined using radiological and clinical findings. We identified subsequent serious injuries involving the patient as a driver using linked administrative data. Secondary outcomes included serious injuries involving the patient as a pedestrian, as a passenger, or other traumatic events (fall, fracture, ankle sprain). We used proportional hazard models accounting for death as a competing risk to test the association of hemispheric laterality and outcomes with and without adjustment for age, sex, discharge modified Rankin Scale score, home location, and prior driving record. Patients were followed through to 2017. Results- Among 26 144 patients with hemispheric cerebrovascular events, 377 subsequent serious traffic injuries as a driver (2.2 per 1000 person-year) were identified over a median follow-up of 6.4 person-years. The rate did not differ by laterality (adjusted hazard ratio, 1.00; 95% CI, 0.82-1.23). The risk of a serious traffic injury as a pedestrian was significantly higher after a right-sided than left-sided event (adjusted hazard ratio, 1.27; 95% CI, 1.02-1.58). Subsequent risks for other traumatic injuries did not differ by laterality of cerebrovascular event. Conclusions- The risk of a serious traffic injury as a pedestrian is substantially higher after a right-hemispheric cerebrovascular event compared with a left-sided event. Walking should be promoted for exercise in survivors of a stroke or transient ischemic attack, but these vulnerable road users may benefit from additional poststroke rehabilitation to optimize safety.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".