Abstract T P123: Impairment in Cognitively Demanding Driving Situations after Acute Mild Ischemic Stroke
Bibliographic record
Abstract
Introduction: Guidelines established by prominent governing bodies recommend that patients should wait a minimum of one month before resuming driving after stroke; however, these guidelines are not based on empirical evidence. Furthermore, many patients report resuming driving within the one-month period post-stroke. The aim of this study was to investigate the driving performance of mild stroke patients within the acute phase of injury. It was hypothesized that patients with acute stroke would exhibit more errors in general (e.g. collisions, speed exceedances, centre line crossings) and during cognitively demanding aspects of driving (i.e. left turns with traffic), but not routine aspects of driving (i.e. straight driving and right turns). Methods: The current study used driving simulator technology (STISIM) to compare the driving performance of 10 patients with acute mild ischemic stroke (NIHSS<7, within 7 days post-stroke) to that of 10 healthy, age- and education-matched controls. Patients and controls completed several driving tasks that increased in complexity, from routine right and left turns to cognitively demanding left turns with traffic, where most accidents occur, and a bus following task, which requires a high degree of sustained attention. Results: On average, stroke patients committed over twice as many errors as controls (12.4 vs 6.0, p< 0.01). Although there was no difference between patients and controls in the number of errors committed during routine right and left turns, patients committed more errors during left turns with traffic (2.4 vs 1.3, p<0.05) and a bus following task (8.2 vs 2.1, p<0.05). Conclusions: Patients with acute mild ischemic stroke may be able to maintain driving performance during basic tasks (e.g. straight driving, right turns) and deficits may become apparent during cognitively complex tasks (e.g. left turns with traffic and bus following). The results highlight the importance of healthcare professionals providing driving advice to their patients post-stroke, particularly in the acute phase of injury. Future longitudinal research is required to determine when patients with mild stroke can safely resume driving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".