The Canadian Neonatal Network: development, evolution, and progress
Bibliographic record
Abstract
Abstract: The Canadian Neonatal Network (CNN) is a group of Canadian investigators who conduct research dedicated to the improvement of neonatal-prenatal health and health care in Canada and internationally. The group was founded in 1995 and now includes all 32 level 3 neonatal intensive care units (NICUs) in Canada. The CNN maintains a standardized neonatal database that includes more than 15,000 infants per year (all gestational ages) and more than 4,000 preterm infants per year born at <33 weeks’ gestation. The Network initially focused on identifying variations in care practices and outcomes of preterm infants in Canada using risk-adjustment models. The network then developed and implemented the Evidence-based Practice for Improving Quality program, a national collaborative, multifaceted quality improvement approach which has led to significant increases in survival without major morbidity among preterm infants born at <33 weeks’ gestation. The CNN is also part of a larger Canadian community of integrated neonatal networks that record data on long-term follow-up, neonatal transport, perinatal care, and neonatal surgical care. The Network’s activities have evolved over time and now include benchmarking, quality improvement, outcomes research, clinical trials, and international collaborations. As the field of neonatology constantly evolves, the CNN’s activities help monitor the impacts of changes and identify and implement better care practices aimed at improving the outcomes of neonates.
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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 | 0.033 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".