P.196 Leading the way: an overview of leadership in Canadian academic neurosurgery
Bibliographic record
Abstract
Background: Leadership drives innovation, patient care and resident education in neurosurgery. This study aims to quantitatively analyze the demographic and professional characteristics of leaders in Canadian academic neurosurgery. Methods: Leaders in remunerated positions, such as department chairs and program directors, from the 14 Canadian Royal College of Physicians and Surgeons-accredited neurosurgery programs were identified using publicly available online resources, chain-referral sampling, and personal communications. Demographic and professional data were collected and analyzed using frequency analyses and exploratory chi-square tests. Results: Thirty-four neurosurgical leaders were identified, predominantly in Québec (29.4%) and Ontario (26.5%). Pediatrics (26.5%) was the most common subspecialty. Over half of leaders held positions in the institution where they trained (52.9%). Among non-Quebec programs, male neurosurgeons were less likely than females to hold leadership positions (p = 0.040, OR = 0.344, 95% CI 0.12-0.99), although males still predominated (18 males vs. 6 females). In Quebec, no gender association was found (p = 0.652). Leaders averaged 76.4 (±81.49) publications and an h-index of 19.71 (±15.12) with nearly two-thirds holding advanced degrees. Conclusions: This study establishes the current landscape of leaders in Canadian academic neurosurgery. Further research is needed to explore career trajectory and barriers to leadership in the field.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".