The Canadian Pediatric Cardiology Research Network: A Model National Data-Sharing Organization to Facilitate the Study of Pediatric Heart Diseases
Bibliographic record
Abstract
BACKGROUND: Common hurdles to pediatric cardiology research include the heterogeneity and relative rarity of specific cardiac malformations, the potential for effect of residual lesions occurring decades after repair, and the scarcity of objective and easily measurable outcomes such as death and transplantation. METHODS: To help meet these challenges, the Canadian Pediatric Cardiology Research Network (CPCRN) was founded by the Canadian Pediatric Cardiology Association to link Canadian academic institutions to promote and facilitate multicollaborations for the benefit of pediatric and congenital cardiology research. The overarching goal of the CPCRN is to build a national framework that harnesses the strong desire for collaboration within the pediatric cardiology community and to identify solutions to barriers that impede multicentre partnerships. RESULTS: In this report, the authors describe the approach and the components of the CPCRN. Specifically, we detail the rolling out of a pan-Canadian master agreement that covers current and future studies, the systematic banking of all project data, and the mechanisms developed to facilitate secondary use of data. CONCLUSIONS: This experience could help guide the formation of other national research groups, particularly those focused on congenital or rare diseases.
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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 | 0.103 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".