The Canadian Consortium for Research in Pediatric Surgery: Roadmap for Creation and Implementation of a National Subspecialty Research Consortium
Bibliographic record
Abstract
Clinical practice should be driven by high-quality research that produces evidence to inform best practices. Generation of such evidence is often challenging, particularly for smaller specialties, such as pediatric surgery, that treat many patients with rare diseases. Multi-institutional collaboration is seen as a major strategy to address these challenges. We have recently created the Canadian Consortium for Research in Pediatric Surgery, a national consortium that includes all major pediatric surgical services across Canada. The mission of the Consortium is to improve pediatric surgical care through high-quality collaborative research. In this article, we describe the rationale and methodology for creation of the Canadian Consortium for Research in Pediatric Surgery, demonstrate its achievements to date, and share a number of foundational concepts that are integral to its success. Our aim is to provide a model for creation of such consortia, ultimately leading to improvements in the quality of clinical research and patient care.
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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 | 0.175 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".