Assessing Fitness to Drive in Brain Tumour Patients: A Grey Matter of Law, Ethics, and Medicine
Bibliographic record
Abstract
BACKGROUND: Neurocognitive deficits from brain tumours may impair the ability to safely operate a motor vehicle. Although certain jurisdictions in Canada legally require that physicians report patients who are unfit to drive, criteria for determining fitness are not clearly defined for brain tumours. METHODS: Patients receiving brain radiotherapy at our institution from January to June 2009 were identified using the Oncology Patient Information System. In addition to descriptive statistics, details of driving assessment were reviewed retrospectively. The Fisher exact test was used to determine factors predictive of reporting a patient to the Ontario Ministry of Transportation (mto) as unfit to drive. A logistic regression model was constructed to further determine factors predictive of reporting. RESULTS: Of the 158 patients available for analysis, 48 (30%) were reported to the mto, and 64 (41%) were advised to stop driving. With respect to the 53 patients with seizures, a report was submitted to the mto for 30 (57%), and a documented discussion about the implications of driving was held with 35 (66%). On univariate analysis, younger age, a central nervous system primary, higher brain radiotherapy dose, unifocal disease, and the presence of seizures were predictive of physician reporting (p < 0.05). On logistic regression modelling, the presence of seizures (odds ratio: 3.9) and a higher radiotherapy dose (odds ratio: 1.3) remained predictive of reporting. INTERPRETATION: Physicians frequently do not discuss the implications of driving with brain tumour patients or are not properly documenting such advice (or both). Clear and concise reporting guidelines need to be drafted given the legal, medical, and ethical concerns surrounding this public health issue.
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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 | 0.037 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".