The Chinese Neonatal Network: a new platform of national collaboration on quality improvement for preterm infants
Bibliographic record
Abstract
Background: The population of China represents about one sixth of the global population. It is important to create a national database with a large sample size and a sustained collaborative platform. The objective of this study was to introduce the Chinese Neonatal Network (CHNN), a new platform of national collaboration on quality improvement (QI) for short and long-term health outcomes of preterm infants. Methods: The background, participating centers, data collection, and objectives are described. Specific objectives are to: (I) Establish a standardized database of very preterm infants (VPI) with gestational age <32 weeks or birth weight (BW) <1,500 grams. (II) Assess, benchmark and monitor major outcomes of VPI and their risk determinants, as well as inter-institutional variations. (III) Implement the Evidence-based Practice for Improving Quality (EPIQ) program to improve quality of care and outcomes. (IV) Conduct collaborative research, including epidemiologic, clinical and health services studies and randomized controlled trials. This study is collaboratively funded by Canadian Institute of Health Research and the Children’s Hospital of Fudan University. Results: The CHNN collaboration acts as a platform that member neonatal intensive care units (NICUs) can access for evidence on implementing practice improvements, learning activities, and research collaborations. There were 58 participating centers in 2018, which was expanded to be 79 in 2021. CHNN has published 2 annual reports to describe the outcomes and care practices of VPI in 2019 and 2020, and conducted an internal audit of data quality. Conclusions: The knowledge generated will be available nationally and worldwide with a definite national impact and a likely global impact.
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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 | 0.101 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".