Feeding the Late and Moderately Preterm Infant
Bibliographic record
Abstract
Nutritional guidelines and requirements for late or moderately preterm (LMPT) infants are notably absent, although they represent the largest population of preterm infants. The European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) Committee on Nutrition (CoN) performed a review of the literature with the aim to provide guidance on how to feed infants born LMPT, and identify gaps in the literature and research priorities.Only limited data from controlled trials are available. Late preterm infants have unique, often unrecognized, vulnerabilities that predispose them to high rates of nutritionally related morbidity and hospital readmissions. They frequently have feeding difficulties that delay hospital discharge, and poorer rates of breastfeeding initiation and duration compared with term infants. This review also identified that moderately preterm infants frequently exhibit postnatal growth restriction.The ESPGHAN CoN strongly endorses breast milk as the preferred method of feeding LMPT infants and also emphasizes that mothers of LMPT infants should receive qualified, extended lactation support, and frequent follow-up. Individualized feeding plans should be promoted. Hospital discharge should be delayed until LMPT infants have a safe discharge plan that takes into account local situation and resources.In the LMPT population, the need for active nutritional support increases with lower gestational ages. There may be a role for enhanced nutritional support including the use of human milk fortifier, enriched formula, parenteral nutrition, and/or additional supplements, depending on factors, such as gestational age, birth weight, and significant comorbidities. Further research is needed to assess the benefits (improved nutrient intakes) versus risks (interruption of breast-feeding) of providing nutrient-enrichment to the LMPT infant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".