P.019 Report from the Canadian Neurosurgery Research Collaborative – One year of resident-led multicentre research initiatives
Bibliographic record
Abstract
Background: The Canadian Neurosurgery Research Collaborative (CNRC) was founded in November 2015 as a resident-led national network for multicentre research. We present an annual report of our activities. Methods: CNRC meetings and publications were reviewed and summarized. The status of ongoing and future studies was collected from project leaders. Results: In its first year, the CNRC produced two papers accepted for publication in the Canadian Journal of Neurological Sciences: A CNRC launch letter and a study of operative volume at Canadian neurosurgery residency programs. Three manuscripts are in preparation: 1) a study of the demographics of Canadian neurosurgery residents, 2) an assessment of mobile devices usage patterns and 3) a validation study of the most utilized neurosurgery mobile apps. In addition, protocols for two multi-centre studies are currently undergoing national Research Ethics Board review: A retrospective study of the incidence and predictors of cerebellar mutism and a prospective registry of external ventricular drain procedures and complications. The network is now a registered not-for-profit organization endorsed by the Canadian Neurosurgical Society. Conclusions: The CNRC is a feasibile, relevant and productive resident-led national research network. As the CNRC matures, we look forward to expanding the scope and impact of its projects.
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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 | 0.049 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".