The Effect of High and Low Antiepileptic Drug Dosage on Simulated Driving Performance in Person’s with Seizures: A Pilot Study
Bibliographic record
Abstract
Background: Prior studies examining driving performance have not examined the effects of antiepileptic drugs (AED’s) or their dosages in persons with epilepsy. AED’s are the primary form of treatment to control seizures, but they are shown to affect cognition, attention, and vision, all which may impair driving. The purpose of this study was to describe the characteristics of high and low AED dosages on simulated driving performance in persons with seizures. Method: Patients (N = 11; mean age 42.1 ± 6.3; 55% female; 100% Caucasian) were recruited from the Epilepsy Monitoring Unit and had their driving assessed on a simulator. Results: No differences emerged in total or specific types of driving errors between high and low AED dosages. However, high AED drug dosage was significantly associated with errors of lane maintenance (r = .67, p < .05) and gap acceptance (r = .66, p < .05). The findings suggest that higher AED dosages may adversely affect driving performance, irrespective of having a diagnosis of epilepsy, conversion disorder, or other medical conditions. Conclusion: Future studies with larger samples are required to examine whether AED dosage or seizure focus alone can impair driving performance in persons with and without seizures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".