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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".