Understanding Intra-Operative Mortality in Canadian Neurosurgery: A Survey-Based Analysis
Bibliographic record
Abstract
Background: Intra-operative mortality in neurosurgery is a rare but profoundly impactful event with significant consequences for patient outcomes, surgical teams, and healthcare systems. Despite advancements in surgical techniques, neurosurgical procedures inherently carry high risks due to the complexity of brain and spinal cord operations. While previous studies have explored patient comorbidities and team dynamics as contributors to surgical outcomes, limited research exists on how these factors influence intra-operative mortality and the coping mechanisms of surgeons following such events. Objectives: This study aims to analyze intra-operative mortality in Canadian neurosurgery by identifying contributing factors related to patient characteristics, surgeon experience, and team dynamics. Additionally, it seeks to evaluate the emotional and professional impact of these events on neurosurgeons. Methods: A cross-sectional survey will be distributed to neurosurgeons across Canada, assessing demographic data, patient risk factors, surgical circumstances, and surgeon/team dynamics associated with intra-operative deaths. The survey will also explore how surgeons cope with these incidents and whether such experiences influence future decision-making. Data will be collected anonymously via REDCap and analyzed using descriptive and inferential statistical methods. Future Directions: Findings from this study will provide valuable insights into modifiable risk factors, inform surgical training programs, and guide institutional support strategies for neurosurgeons dealing with intra-operative mortality. Understanding these elements will contribute to enhanced patient safety, improved surgical decision-making, and better psychological support mechanisms for neurosurgeons.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".