F.6 Cranial neurosurgery medicolegal cases in Canada: a ten-year analysis of Canadian Medical Protective Association (CMPA) data
Bibliographic record
Abstract
Background: Neurosurgery is a high-risk specialty with a low margin of error. We aim to assess the risk of neurosurgeons being involved in medicolegal cases in Canada. Methods: This retrospective descriptive study evaluated ten years (2012-2021) of closed legal cases, college cases, and hospital complaints against neurosurgeons with data from the CMPA. Included cases were cranial cases, VP shunts, or cases where a catheter or wire was inserted into the brain. Cases excluded angiography, radiation, ultrasound, or percutaneous procedures. Results: We identified 77 cases (66 urgent or emergent). Neurosurgeons had a significantly higher medicolegal risk than the CMPA surgeon membership, however lower risk compared to all physician specialties. Legal cases accounted for 69% with favourable outcomes in 52%. Forty-one cases involved post-operative complications and 16 cases involved VP shunts. Multiple surgeons or residents could be involved spanning age groups and years in practice. Thirty-four cases had a harmful incident, 41% of these severe. The majority of cases occurred at urban centers. The average case duration was 41 months. Conclusions: This study provides a recent medicolegal analysis of cranial neurosurgery in Canada. We identified areas of common complaints and hope the data can be used to mitigate risk surgical risk in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".