Trends and contributing factors in medicolegal cases involving cranial surgery in Canada
Bibliographic record
Abstract
OBJECTIVE: Neurosurgery is a high-risk specialty with a low margin of error, and neurosurgeons have a higher medicolegal risk than practitioners in many other specialties. The aim of this study was to provide a current medicolegal landscape of cranial surgery in Canada. METHODS: In this retrospective descriptive study, the authors evaluated 10 years (2012-2021) of cranial neurosurgical data on closed legal actions, medical regulatory authority (College) cases, and hospital complaints against neurosurgeons that had been submitted to the Canadian Medical Protective Association (CMPA). Only cranial cases, even those involving ventriculoperitoneal shunt (VPS) placement or catheter or wire insertion in the brain, were eligible for study inclusion. Excluded cases were those involving pediatric patients and angiography, radiation, ultrasound, or percutaneous procedures. RESULTS: Seventy-six cranial cases were included in the study. Neurosurgeons had a significantly higher medicolegal risk compared to that of the overall CMPA surgeon membership. Civil legal actions accounted for more than half of all the cranial cases. Fifty-four percent of cases involved postoperative complications, and 21% involved VPS placement. Communication issues were commonly named factors leading to a medicolegal complaint throughout the data. CONCLUSIONS: This is the first report on the Canadian experience of medicolegal cranial surgery cases. These cases most commonly involved tumor excision, VPS insertion, and decompressive craniectomy. The VPS cases were unexpectedly common and should be further investigated. A breakdown in communication was a major theme in the medicolegal data repository.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".