The Canadian Preterm Birth Network: a study protocol for improving outcomes for preterm infants and their families
Bibliographic record
Abstract
BACKGROUND: Preterm birth (birth before 37 wk of gestation) occurs in about 8% of pregnancies in Canada and is associated with high mortality and morbidity rates that substantially affect infants, their families and the health care system. Our overall goal is to create a transdisciplinary platform, the Canadian Preterm Birth Network (CPTBN), where investigators, stakeholders and families will work together to improve childhood outcomes of preterm neonates. METHODS: Our national cohort will include 24 maternal-fetal/obstetrical units, 31 neonatal intensive care units and 26 neonatal follow-up programs across Canada with planned linkages to provincial health information systems. Three broad clusters of projects will be undertaken. Cluster 1 will focus on quality-improvement efforts that use the Evidence-based Practice for Improving Quality method to evaluate information from the CPTBN database and review the current literature, then identify potentially better health care practices and implement identified strategies. Cluster 2 will assess the impact of current practices and practice changes in maternal, perinatal and neonatal care on maternal, neonatal and neurodevelopmental outcomes. Cluster 3 will evaluate the effect of preterm birth on babies, their families and the health care system by integrating CPTBN data, parent feedback, and national and provincial database information in order to identify areas where more parental support is needed, and also generate robust estimates of resource use, cost and cost-effectiveness around preterm neonatal care. INTERPRETATION: These collaborative efforts will create a flexible, transdisciplinary, evaluable and informative research and quality-improvement platform that supports programs, projects and partnerships focused on improving outcomes of preterm 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 | 0.076 | 0.068 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".