A Case–Control Study Investigating Simulated Driving Errors in Ischemic Stroke and Subarachnoid Hemorrhage
Bibliographic record
Abstract
Background: Stroke can affect a variety of cognitive, perceptual, and motor abilities that are important for safe driving. Results of studies assessing post-stroke driving ability are quite variable in the areas and degree of driving impairment among patients. This highlights the need to consider clinical characteristics, including stroke subtype, when assessing driving performance. Methods: We compared the simulated driving performance of 30 chronic stroke patients (>3 months), including 15 patients with ischemic stroke (IS) and 15 patients with subarachnoid hemorrhage (SAH), and 20 age-matched controls. A preliminary analysis was performed, subdividing IS patients into right (n = 8) and left (n= 6) hemispheric lesions and SAH patients into middle cerebral artery (MCA, n = 5) and anterior communicating artery (n = 6) territory. A secondary analysis was conducted to investigate the cognitive correlates of driving. Results: Nine patients (30%) exhibited impaired simulated driving performance, including four patients with IS (26.7%) and five patients with SAH (33.3%). Both patients with IS (2.3 vs. 0.3, U = 76, p0.6, p<0.05). No cognitive tests showed utility amongst patients with SAH. Conclusions: Both IS and SAH exhibited difficulty with lane maintenance. Patients with IS additionally exhibited difficulty with speed maintenance, whereas SAH patients exhibited difficulty turning performance. Current results support the importance of differentiating between stroke subtypes and considering other important clinical characteristics (e.g. side of lesion, vascular territory) when assessing driving performance and reinforce the importance of physicians discussing driving safety with patients after stroke.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".