Operative Landscape at Canadian Neurosurgery Residency Programs
Bibliographic record
Abstract
Background Currently, the literature lacks reliable data regarding operative case volumes at Canadian neurosurgery residency programs. Our objective was to provide a snapshot of the operative landscape in Canadian neurosurgical training using the trainee-led Canadian Neurosurgery Research Collaborative. METHODS: Anonymized administrative operative data were gathered from each neurosurgery residency program from January 1, 2014, to December 31, 2014. Procedures were broadly classified into cranial, spine, peripheral nerve, and miscellaneous procedures. A number of prespecified subspecialty procedures were recorded. We defined the resident case index as the ratio of the total number of operations to the total number of neurosurgery residents in that program. Resident number included both Canadian medical and international medical graduates, and included residents on the neurosurgery service, off-service, or on leave for research or other personal reasons. RESULTS: Overall, there was an average of 1845 operative cases per neurosurgery residency program. The mean numbers of cranial, spine, peripheral nerve, and miscellaneous procedures were 725, 466, 48, and 193, respectively. The nationwide mean resident case indices for cranial, spine, peripheral nerve, and total procedures were 90, 58, 5, and 196, respectively. There was some variation in the resident case indices for specific subspecialty procedures, with some training programs not performing carotid endarterectomy or endoscopic transsphenoidal procedures. CONCLUSIONS: This study presents the breadth of neurosurgical training within Canadian neurosurgery residency programs. These results may help inform the implementation of neurosurgery training as the Royal College of Physicians and Surgeons residency training transitions to a competence-by-design curriculum.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".