The Risk of Motor Vehicle Crashes and Traffic Citations Post Stroke: A Structured Review
Bibliographic record
Abstract
BACKGROUND: Stroke impacts the domains known to be important for driving and is a primary condition for driving evaluation referrals. Given the high prevalence of stroke, the objective was to summarize the evidence regarding risk of crashes and traffic citations post stroke. METHODS: A structured review of six databases was conducted to retrieve studies that included stroke as a separate exposure from other disorders and measured crashes or traffic citations as an outcome. RESULTS: Four cohort and three case-control studies met the inclusion criteria. Five of the seven studies found increased odds or risk ratios ranging from 1.9 to 7.7, while two found an association of 0.8. Only one result was statistically significant (RR=2.7). One study examined the outcome traffic citations and found no significant association. CONCLUSION: There is cause for concern regarding increased risk of crashes post stroke. Future studies that examine the impact of stroke severity and sequelae will help health professionals, families, and those with stroke make informed decisions regarding driving post stroke. This review indicates that drivers with stroke have an increased risk of crashing compared to their counterparts without stroke, as demonstrated by increased risk estimates in five out of the seven studies that have examined this issue. This review also points to an urgent need for rigorous studies investigating the risk of crashes according to specific stroke sequelae: an understanding of crash risk based on stroke severity, impairments, and function will assist clinicians in making informed decisions regarding the need for comprehensive driving evaluation and the potential for driver retraining for specific subgroups.
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How this classification was reachedexpand
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".