First Seizure While Driving (FSWD) — An Underestimated Phenomenon?
Bibliographic record
Abstract
BACKGROUND: Seizures while driving are a well known occurrence in established epilepsy and have significant impact on driving privileges. There is no data available on patients who experience their first (diagnosed) seizure while driving (FSWD). METHOD: Out of 311 patients presenting to the Halifax First Seizure Clinic between 2008 and 2011, 158 patients met the criteria of a first seizure (FS) or drug-naïve, newly diagnosed epilepsy (NDE). A retrospective chart review was conducted. FSWD was evaluated for 1) prevalence, 2) clinical presentation, 3) coping strategies, and 4) length of time driving before seizure occurrence. RESULTS: The prevalence of FSWD was 8.2%. All 13 patients experienced impaired consciousness. Eleven patients had generalized tonic-clonic seizures, one starting with a déjà-vu evolving to visual aura and a complex partial seizure; three directly from visual auras. Two patients had complex partial seizures, one starting with an autonomic seizure. In response to their seizure, patients reported they were i) able to actively stop the car (n=4, three had visual auras), ii) not able to stop the car resulting in accident (n=7), or iii) passenger was able to pull the car over (n=2). One accident was fatal to the other party. Twelve out of 13 patients had been driving for less than one hour. DISCUSSION: FSWD is frequent and possibly underrecognized. FSWD often lead to accidents, which occur less if preceded by simple partial seizures. Pathophysiological mechanisms remain uncertain; it is still speculative if complex visuo-motor tasks required while driving play a role in this scenario.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".