P.015 Demographics of Canadian neurosurgery residents – a national cross-sectional study from the Canadian Neurosurgery Research Collaborative
Bibliographic record
Abstract
Background: The Canadian Neurosurgery Research Collaborative (CNRC) is a new consortium of neurosurgery residency programs set-up to facilitate the planning and implementation of multi-center studies. As a trainee-led organization, it will focus on resident-initiated, resident-driven projects. The goal of this study is to assess the demographics of Canadian neurosurgery residents, with particular focus on their academic and subspecialty interests. Methods: After approval by the CNRC, an online survey will be sent to all Canadian neurosurgery residents and fellows with reminders at 2, 4 and 6 weeks. Anonymous, basic demographic data will be collected. Specific interest towards the various subspecialties, research and academic vs community practice will be measured. The data will be crossed with the ongoing Canadian Neurosurgery Operative Landscape study to assess the impact of case volume on academic and subspecialty interests. Results: This is the first study providing a snapshot of Canadian neurosurgery residents at all levels of training. The study is ongoing and the official results will be presented at the meeting. As one of the first CNRC studies, it will also demonstrate the effectiveness of the collaborative. Conclusions: Understanding the demographics and interests of Canadian neurosurgery residents will allow the CNRC to better fulfill its mission.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".