Survey on human milk feeding and enteral feeding practices for very-low-birth-weight infants in NICUs in China Neonatal Network
Bibliographic record
Abstract
BACKGROUND: The breastfeeding rate in China is lower than that in many other countries and the extent of adoption of the "Feeding Recommendations for Preterm Infants and Low Birth Weight Infants" guideline in NICUs remains unclear. METHOD: A web-based survey about the current status of human milk feeding and enteral feeding practices at NICUs was sent to all China Neonatal Network's cooperation units on September 7, 2021, and the respondents were given a month to send their responses. RESULTS: All sixty NICUs responded to the survey, the reply rate was 100%. All units encouraged breastfeeding and provided regular breastfeeding education. Thirty-six units (60.0%) had a dedicated breastfeeding/pumping room, 55 (91.7%) provided kangaroo care, 20 (33.3%) had family rooms, and 33 (55.0%) routinely provided family integrated care. Twenty hospitals (33.3%) had their own human milk banks, and only 13 (21.7%) used donor human milk. Eight units (13.3%) did not have written standard nutrition management guidelines for infants with body weight < 1500 g. Most units initiated minimal enteral nutrition with mother's milk for infants with birth weight ˂1500 g within 24 h after birth. Fifty NICUs (83.3%) increased the volume of enteral feeding at 10-20 ml/kg daily. Thirty-one NICUs (51.7%) assessed gastric residual content before every feeding session. Forty-one NICUs (68.3%) did not change the course of enteral nutrition management during drug treatment for patent ductus arteriosus, and 29 NICUs (48.3%) instated NPO for 1 or 2 feeds during blood transfusion. CONCLUSION: There were significant differences in human milk feeding and enteral feeding strategies between the NICUs in CHNN, but also similarities. The data obtained would be useful in the establishment of national enteral feeding guidelines for preterm infants and quality improvement of cooperation at the national level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".