A Quality Improvement Initiative to Increase Mother's Own Milk Use in a Chinese Neonatal Intensive Care Unit
Bibliographic record
Abstract
Background: To evaluate the efficacy of a multidisciplinary quality improvement intervention to promote mother's own milk feeding and reduce necrotizing enterocolitis (NEC) in very low-birth-weight infants. Materials and Methods: We conducted a pre (January 2014 to March 2015)–post (April 2015 to June 2016), nonrandomized, interventional cohort study of infants born at <1,500 g birth weight and admitted to the Fudan University Children's Hospital level III neonatal intensive care unit in Shanghai. The intervention included establishing a breast milk promotion team and breast milk pumping room, educating staff and parents, and distributing teaching materials. The primary outcome was breast milk feeding rate. Secondary outcomes included incidences of NEC, NEC needing surgery, mortality, and time to full enteral feeds. Results: A total of 488 infants (210 baseline, 278 intervention) <1,500 g were enrolled. The intervention group had significantly increased feeding rates for any mother's milk (34.76% vs. 80.58%; p < 0.01) and high-volume mother's milk (≥50% of feeds; 22.86% vs. 61.15%; p < 0.01), and decreased incidence of NEC needing surgery (7.62% vs. 3.24%; adjusted odds ratio [OR] 0.32, 95% confidence interval [CI] 0.14–0.76). There were no significant differences in rates of mortality (0.5% vs. 1.49%; adjusted OR 2.10, 95% CI 0.22–19.6), NEC (10.00% vs. 7.55%; adjusted OR 0.59, 95% CI 0.31–1.14), and time to full enteral feeds (20.18 ± 1.67 days vs. 24.15 ± 1.65 days; adjusted OR = 1.09, 95% CI 0.99–1.21). Conclusions: Our quality improvement initiative increased the consumption of mother's own milk and reduced the severity of NEC in very low-birth-weight infants.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".