Fitness to Drive in Patients with Brain Tumours: The Influence of Mandatory Reporting Legislation on Radiation Oncologists in Canada
Bibliographic record
Abstract
BACKGROUND: Certain jurisdictions in Canada legally require that physicians report unfit drivers. Physician attitudes and patterns of practice have yet to be evaluated in Canada for patients with brain tumours. METHODS: We conducted a survey of 97 radiation oncologists, eliciting demographics, knowledge of reporting laws, and attitudes on reporting guidelines for unfit drivers. Eight scenarios with varying disability levels were presented to determine the likelihood of a patient being reported as unfit to drive. Statistical comparisons were made using the Fisher exact test. RESULTS: Of physicians approached, 99% responded, and 97 physicians participated. Most respondents (87%) felt that laws in their province governing the reporting of medically unfit drivers were unclear. Of the responding physicians, 23 (24%) were unable to correctly identify whether their province had mandatory reporting legislation. Physicians from provinces without mandatory reporting legislation were significantly less likely to consider reporting patients to provincial authorities (p = 0.001), and for all clinical scenarios, the likelihood of reporting significantly depended on the physician's provincial legal obligations. CONCLUSIONS: The presence of provincial legislation is of primary importance in determining whether physicians will report brain tumour patients to drivers' licensing authorities. In Canada, clear guidelines have to be developed to help in the assessment of whether brain tumour patients should drive.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".