Impact of Mandatory Physician Reporting on Accident Risk in Epilepsy
Bibliographic record
Abstract
BACKGROUND: In some jurisdictions, physicians are required by law to report patients with seizures to the department of motor vehicles. We assessed the hypothesis that mandatory reporting reduces the risk of automobile accidents in people with epilepsy. METHODS: A retrospective survey of driving and accident rates was done by mailed questionnaire to two groups of subjects with epilepsy in Canada, one living in Ontario where reporting is mandatory and the other in Alberta where it is not. Responses were obtained from a control group without epilepsy for comparison. RESULTS: The epilepsy (n = 425) and control (n = 375) groups were comparable in age and sex. Seventy-three percent of the epilepsy group were or had been licensed drivers compared to 94% of the controls (rr 0.77, 95% CI 0.73-0.83, p < 0.001). Lifetime accident rate of licensed drivers was 58% in epilepsy and 60% in controls (rr 0.99, 95%CI 0.82-1.19, ns) while 9% of the epilepsy group and 9% of the controls had an accident in the previous year (rr 1.00, 95%CI 0.95-1.06, ns). All those with epilepsy in Ontario (n = 202) and Alberta (n = 223), also comparable in age and sex, had equal lifetime accident rates of 45 and 46% (rr 0.99, 95%CI 0.67-1.47, ns) and 1-year rates of 11 and 8% (rr 1.38, 95%CI 0.59-3.27, ns). In Ontario, 20% of drivers were unlicensed compared to 9% in Alberta (rr 2.39, 95%CI 1.17-4.89, p = 0.01) CONCLUSION: Although it is clearly dangerous for many people with ongoing seizures to drive, the findings provide no support for the hypothesis that mandatory reporting of patients by physicians reduces accident risk and suggest that concerns about the impact of epilepsy on driving compared to other medical and nonmedical risk factors may be excessive.
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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 | 0.003 | 0.040 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".