P.010 The Canadian Neurosurgery Research Collaborative (CNRC): A novel, trainee-led, nationwide multicentre research network
Bibliographic record
Abstract
Background: The goals of evidence-based neurosurgery are to improve surgical outcomes, reduce complications, and provide an objective basis for altering practice. The need for higher quality studies, typically prospective and multicentre, has been growing especially in light of the evolving complexity of neurosurgical interventions and heterogeneity of patient populations. In the United Kingdom (UK), trainee-led research collaboratives have been established to tackle this problem. Therefore, we sought to evaluate the potential role for a resident-led research collaborative in neurosurgery in Canada based on the UK experience. Methods: A literature review of trainee-led collaboratives was conducted utilizing PubMed and Medline. Identified articles were reviewed for study quality and clinical relevance to explore the potential benefits of collaboratives. Results: In the UK, 27 collaboratives have been established in various specialties by trainees. Some published high quality trials with implications on their clinical fields. Evidence suggests that such endeavors improves trainees’ research skills and may help cultivate a research culture tailored towards clinical trials. Conclusions: Given the growing evidence for research collaboratives in the UK, we propose launching the Canadian Neurosurgery Research Collaborative (CNRC) which currently represents 12 out of 14 neurosurgery programs in Canada, and planning its first multicenter prospective study.
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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 | 0.062 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".