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 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 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".