Counseling at a Seizure Clinic Does Not Ensure Disclosure to the Transportation Registry
Bibliographic record
Abstract
BACKGROUND: The effectiveness of current self-reporting driving laws for medically-unfit potential seizure patients is unknown in Canada. We designed a prospective cohort study of patients' self-reporting practices to the local Transportation Registry (TR) and their driving behaviors following detailed counselling at a seizure clinic in a discretionary physician-reporting jurisdiction. METHODS: Medically unfit drivers, referred to our seizure clinic, who had a valid driver's permit at the time of their episode of impaired consciousness were included. Patients' self-reporting and driving behaviours were assessed using a standardized interview prior to a neurologist's counseling and later at a follow-up visit. RESULTS: Sixty three patients were included; 77% were diagnosed as having had a seizure at the time of their referral. Prior to their seizure clinic visit, 3/63 (5%) had been counseled to self-report to the TR by a non-neurologist physician, and none had done so. Following a neurologist's documented counseling 34/63 (54%) had self-reported themselves at the follow-up seizure clinic visit, and 53/63 (84%) were not driving. CONCLUSION: This prospective study design is the first in North America to examine self-reporting rates for unfit drivers with a seizure disorder. Our findings suggest that self-reporting laws do not ensure high rates of self-reporting behaviors even when patients seen at a seizure clinic are appropriately counseled of their legal obligations. The rate of driving cessation appears greater than the rate of self-reporting to the TR among counseled patients.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".