C.05 Is neurosurgical resident training safe?
Bibliographic record
Abstract
Background: With the emergence of competency-based residency education (CBME) in Europe and North America, supervised operative experience is essential for residents to demonstrate competency in requisite neurosurgical procedures prior to board certification. This study explores the implications of such operative exposure to patient safety. Methods: Using a pro- and retrospectively maintained databank at two Swiss teaching hospitals, we compared complications, revision surgery rates, and outcome of consecutive patients undergoing lumbar microdiscectomy (n=102), lumbar decompression (n=471), anterior cervical discectomy and fusion (n=281), cranioplasty (n=240), shunt implantation (n=200), and epidural steroid injections (n=354) by a supervised resident versus a board-certified faculty neurosurgeon as primary surgeon using logistic regression. Results: Intra- (OR 0.68, 95%CI 0.33–1.41, p=0.305) and postoperative complications (OR 1.14, 95%CI 0.78–1.65, p=0.49), revision surgeries (OR 1.23, 95%CI 0.78–1.93, p=0.36), operating time (p=0.87), blood loss (p=0.57) and the likelihood to be considered treatment responder (OR 0.91, 95%CI 0.65–1.28, p=0.62) was similar for both groups. Specifics of European and Canadian neurosurgery training are compared and discussed. Conclusions: Hands-on surgical education within the framework of a structured residency-training program is safe in cervical and lumbar spine surgery and for standard cranial procedures. The summarized results in conjunction with the literature suggest that CBME in Europe and Northern America would not compromise patient safety.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.200 | 0.030 |
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".