P.021 Seizures among drivers in Newfoundland and Labrador
Bibliographic record
Abstract
Background: Regulation of drivers licences aims to strike a balance between autonomy and public safety. In Newfoundland and Labrador, an individual experiencing seizures must have a 6-month seizure-free interval before a driving licence is reinstated, although exceptions apply. There is a paucity of data surrounding driving safety in people with epilepsy. Methods: The Department of Motor Vehicles and Registration extracted data from the charts of drivers experiencing seizures for the period between 2010-2014, inclusive. Two groups were examined: drivers aged 16-24 (n=104) and 75+ (n=115). Given that mandatory reporting is required in Newfoundland and Labrador, this theoretically represents a population-based study. Results: Overall, 5.1% of the population experienced a motor vehicle collision, and collisions were more frequent among younger drivers. Significantly more people in the 75+ category had a medical history that could have contributed to seizures. Only 37.6% of the overall sample had their first seizure reported. This was not different between age groups or seizure types (generalized vs. focal). Though the age groups differed with respect to seizure type, this did not affect driving safety, as measured by motor vehicle collisions and driving disobedience. Conclusions: We found a high rate of driving disobedience despite the requirement for mandatory reporting and seizure type did not affect driving safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".