Multidisciplinary assessment and reporting of fitness to drive in brain tumor patients: A gray matter.
Bibliographic record
Abstract
6130 Background: In some jurisdictions, there is a legal requirement for physicians to report medically unfit drivers. Objectives of this study are to determine physician knowledge and attitudes on reporting legislation and driving assessment, and review our institution’s experience in evaluating fitness to drive in brain tumour patients. Methods: Physicians caring for brain tumour patients in South-western Ontario were identified by public databases and surveyed by mail. Survey questions elicited demographics, opinions, and factors influencing the decision to report. Patients receiving brain radiotherapy at our institution between January and June 2009 were identified and details of driving assessment were extracted. Fisher’s exact test and a logistic regression model were used to determine differences in responses between specialists and family physicians and factors influencing reporting. Results: Surveys (n=467) were distributed with 198 (43%) responses. Most (76%) felt that reporting guidelines were unclear. Neurologists (43%) and Family Physicians (22%) were felt to be the most responsible to report unfit drivers. Compared to specialists, Family Physicians were less likely: to be comfortable with reporting (p=0.02), to consider reporting (p<0.001), or discuss the implications of driving (p<0.001). Perceived barriers in assessing fitness to drive included: lack of tools to assess (57%) and the impact on the patient-physician relationship (34%). 158 patients were retrospectively reviewed. Forty-eight patients (30%) were reported to the provincial licensing authority and 64 (41%) were advised not to drive. 53 patients experienced seizures, of which 36 (68%) had a documented discussion on driving. Only 30 (56%) of these patients were reported to the licensing authority despite legal requirements. Age, primary disease, previous neurosurgery and seizures were predictive of reporting (p<0.05). On logistic regression modeling, seizures (OR 12.4) and primary CNS disease (OR 15.5) remained predictive of reporting. Conclusions: Despite guidelines and laws, the assessment of fitness to drive in patients with brain tumours is not routinely conducted or documented in a multidisciplinary setting.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".