A Multi-Center Survey of Necrotizing Enterocolitis Prevention Strategies in Very Low Birth Weight Infants
Bibliographic record
Abstract
Xiaoshan Hu, Miao Qian, Wenjuan Chen, Shushu Li, Xiaohui Chen, Shuping Han Department of Pediatrics, Nanjing Women and Children’s Healthcare Hospital, Nanjing City, Jiangsu Province, People’s Republic of ChinaCorrespondence: Shuping Han, Department of Pediatrics, Nanjing Women and Children’s Healthcare Hospital, Nanjing City, Jiangsu Province, People’s Republic of China, Tel +86 025-52226578, Email shupinghan@njmu.edu.cnObjective: To compare the prevention practices of necrotizing enterocolitis (NEC) across 17 neonatal intensive care units (NICUs) in China.Methods: A web-based survey was sent to 17 level 3 NICUs in China on September 21, 2023, to evaluate the prevention strategies for NEC.Results: All 17 Neonatal Intensive Care Units (NICUs) responded to the survey. There was significant variation in the initial empirical use of antibiotics for early-onset sepsis, late-onset sepsis, and NEC among different NICUs. Out of the 17 NICUs, only 5 (29.4%) used donor human milk. Additionally, 15 (88.2%) NICUs performed routine echocardiography (Echo) in preterm infants after birth to evaluate cardiac function and/or Patent Ductus Arteriosus (PDA) status. Out of those 15 NICUs, 11 (73.3%) performed Echo within 24 to 72 hours after birth. Furthermore, 8 NICUs (47.1%) did not alter enteral nutrition management during drug treatment for PDA, while 12 NICUs (70.6%) stopped 1 or 2 feeds during red blood cell transfusion.Conclusion: The findings of this survey conducted through questionnaires revealed both differences and similarities in the strategies employed to prevent NEC in 17 NICUs in China.Keywords: very low birth weight infants, necrotizing enterocolitis, survey, prevention strategies
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".