Human Milk Feeding Status of Preterm Infants in Neonatal Intensive Care Units in China
Bibliographic record
Abstract
BACKGROUND: Previous low human milk feeding rates in Chinese neonatal intensive care units of preterm infants were reported. There are no nationwide data on these. RESEARCH AIMS: To investigate the current status of human milk feeding for preterm infants in Chinese units and provide baseline data for future research. METHODS: A secondary data analysis was conducted from a previously established clinical database including 25 Chinese neonatal intensive care units. All infants born <34 weeks gestation and admitted to participating units from May 2015 to April 2018 were enrolled. Variables analyzed were infant data collected and the human milk feeding practices at participating units were surveyed. RESULTS: A total of 24,113 infants were included. The overall and exclusive human milk feeding rates were 58.2% and 18.8%, respectively, which increased significantly during study years. We found that rates of human milk feeding decreased with increase in gestational age and birth weight. There was significant variation in human milk feeding rates among units. Most participating Chinese neonatal intensive care units have taken measures to improve the rates of human milk feeding. CONCLUSIONS: The human milk feeding rates in Chinese neonatal intensive care units have continued to increase in the past 3 years, but there was significant variation among them. More efforts are needed to further increase the human milk feeding rates in China. TRIAL REGISTRATION: This study was registered NCT02600195 with clinicaltrials.gov on November 9, 2015.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".