Effect of Multi-Nutrient Milk Fortification on Preterm Neonate Outcomes: A Network Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Optimal feeding regimens for preterm neonates, including the role of multi-nutrient fortification, are unknown, leading to large practice variation in comparing different feeding regimens that include fortification and their impact on outcomes for preterm infants. Methods: Using a network meta-analyses design, two reviewers independently extracted data. A Cochrane CENTRAL, Medline, Embase, and CINAHL search was conducted for all studies published up to 27 June 2023. Randomized clinical trials of feeding regimens for preterm infants that included multi-nutrient fortification were included. Outcomes were mortality, necrotizing enterocolitis (NEC), retinopathy of prematurity (ROP), sepsis, periventricular leukomalacia (PVL), bronchopulmonary dysplasia (BPD), time to full enteral feeds, and the Bayley II MDI developmental score. Results: Fifty-nine studies were included. For mortality, NEC, and time to reach full enteral feeds, the top-ranked treatment class was the mother’s own milk with donor milk and a human-milk-based fortifier. For ROP and BPD, the top-ranked treatment class was mother’s own milk with a phosphorus fortifier. For sepsis, the top-ranked treatment class was mother’s own milk with formula. For PVL, the top-ranked treatment classes were mother’s own milk and mother’s own milk with donor milk and a bovine fortifier in the two disconnected networks. For the Bayley II MDI score, the top-ranked treatment class was mother’s own milk with formula and bovine fortification. Conclusions: Treatment rankings are consistent with the underlying hypothesis that increased mother’s own milk intake appears to be associated with better clinical outcomes. This review provides the first global view of interventions and highlights insufficient high-quality evidence to support or refute one fortification feeding regimen over another.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".