P.137 Implementation of Canadian driving guidelines following cranial procedures: a systematic review and survey of Canadian neurosurgeons
Bibliographic record
Abstract
Background: Following craniotomy, there is widespread agreement that post-operative neurological impairments require specialized evaluation to evaluate fitness to drive. However, for patients who had a craniotomy and do not have neurological deficits or known seizures, there is less consensus as to when return to driving is safe. In this study, we aim to review existing guidelines regarding driving post-craniotomy and assess the current practices for post-craniotomy recommendations in Canada. Methods: Our study has three components: 1) systematic review of existing guidelines for return to driving after cranial procedure; 2) review of primary evidence (cohort studies) regarding seizure risk following a craniotomy, depending of the underlying pathology; 3) online questionnaire distributed to Canadian neurosurgeons by the Canadian Neurosurgery Collaborative (CNRC) network. Results: Our systematic review unveiled various sets of guidelines for driving after a craniotomy. For instance, UK Driving and Vehicle Licensing Agency writes into law specific guidelines for return to driving varying based on underlying pathology. Their results were drawn from large cohort studies measuring the occurrence of post-operative seizures after craniotomy for a variety of conditions. The questionnaire is currently being distributed to Canadian neurosurgeons. Conclusions: Our study lays the first steps towards the development of Canadian guidelines for return to driving post-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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".