Introducing evidence-based practice improvement in Chinese neonatal intensive care units
Bibliographic record
Abstract
China has the largest population in the world. With rapid economic growth, the incidence of premature birth has shown an increasing trend and more neonatal intensive care units (NICUs) are being established across the country. However, there is substantial variability in clinical practice and variations in short- and long-term outcomes among patients in different NICUs. There remains a big gap between China and developed countries in terms of infant outcomes. The Evidence-based Practice for Improving Quality (EPIQ) is a successful model that has been implemented in NICUs across Canada to improve infant outcomes. We applied EPIQ in a single NICU in china and successfully reduced the incidence of ventilator-associated pneumonia, central line (CL) associated bloodstream infection (CLABSIs), and improved the breastmilk use in NICU. In the next phase, we are extending EPIQ to another 24 centers in China and have established the Chinese Neonatal Network for national collaboration, to improve infant outcomes across China.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".