Return to Driving after a Craniotomy: A Systematic Review and Evidence-Based Approach
Bibliographic record
Abstract
BACKGROUND: Patients undergoing craniotomy experience a higher risk of seizures in the ensuing months. Consensus is lacking regarding the appropriate timeframe for safe return to driving following craniotomy in patients not otherwise limited by neurological deficits or a history of epilepsy. METHODS: We performed a systematic literature review on driving recommendations post-craniotomy. We then performed a scoping review on the risk of seizure post-craniotomy and used risk calculations and accepted risk thresholds from the epilepsy literature to develop an evidence-based approach to driving recommendations post-craniotomy. RESULTS: The systematic review of driving recommendations revealed national guidelines (the United Kingdom, New Zealand, Australia). We transposed risk calculations and accepted risk thresholds from the epilepsy literature (accident risk ratio [ARR] < 2; chance of occurrence of a seizure in the next year < 20%) to patients who undergo a craniotomy. Using data from a large meta-analysis of seizure risk post-craniotomy, we calculated ARRs for various underlying pathologies at different postoperative timepoints and compared them with accepted risk thresholds from the epilepsy literature. We determine that patients who undergo a craniotomy for a higher-risk condition (like high-grade glioma) may resume driving after at least 1 month without seizure, whereas those patients undergoing a craniotomy for lower-risk conditions (like infratentorial pathology) may resume driving without consideration for the risk of seizure. CONCLUSION: This systematic review of the literature and evidence-based approach to risk threshold calculations derived from the epilepsy literature provides a preliminary framework to guide clinicians regarding recommendations for return to driving following craniotomy.
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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 | 0.018 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".