Clinical evaluation of fitness to drive in patients with brain metastases
Bibliographic record
Abstract
BACKGROUND: Guidelines to provide recommendations about driving restrictions for patients with brain metastases are lacking. We aim to determine whether clinical neurologic examination is sufficient to predict suitability to drive in these patients by comparison with an occupational therapy driving assessment (OTDA). METHODS: We prospectively evaluated the concordance between neurology assessment of suitability to drive (pass/fail) and OTDA in 41 individuals with brain metastases. Neuro-oncology evaluation included an interview and neurological examination. Participants subsequently underwent OTDA during which a battery of objective measures of visual, cognitive, and motor skills related to driving was administered. RESULTS: = .0046). The sensitivity of the neurology assessment to predict driving fitness compared with OTDA was 16.1% and the specificity 90%. The 31 patients who failed OTDA were more likely to fail Vision Coach, Montreal Cognitive Assessment, and Trail Making B tests. CONCLUSIONS: There was poor association between the assessment of suitability to drive by neurologists and the outcome of the OTDA in patients with brain metastases. Subtle deficits that may impair the ability to drive safely may not be evident on neurologic examination. The positive predictive value was high to predict OTDA failure. Age could be a factor affecting OTDA performance. The results raise questions about the choice of assessments in making recommendations about driving fitness in people with brain metastases. OTDA should be strongly considered in patients with brain metastases who wish to continue driving.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".