The Association of Different Proportions of Human Milk of the Total Enteral Intake on Health Outcomes in Preterm Infants: A Systematic Review
Bibliographic record
Abstract
Background:Human milk (HM) is a proven optimal food for preterm infants. However, there is uncertainty regarding the effects of different proportions of HM of the total enteral intake on health outcomes in preterm infants. Therefore, we conducted a systematic review of studies examining the effects of different proportions of HM of the total enteral intake on health outcomes in preterm infants. Methods:We conducted a literature search in the Web of Science, PubMed, and Scopus databases. The methodological quality of the included articles and the certainty of evidence were assessed according to the Newcastle-Ottawa Scale and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool, respectively. Results:Twelve studies were included in the review. Among the clinical findings, the effect of different proportions of HM of the total enteral feeding on health outcomes in preterm infants was divided into six primary outcomes: physical growth, length of stay, morbidity of any disease, all-cause mortality, feeding-related outcomes, and other health outcomes. The studies presented a high risk of bias for most of the domains. The certainty of the evidence was considered low or very low. Conclusions:The findings reiterated that greater proportions of HM positively affect the health outcomes of preterm infants. Overall, when the HM accounts for at least 20% of the total enteral intake, it has an effect on health outcomes in preterm infants. If the proportion of HM reaches 50%, the incidence and severity of necrotizing enterocolitis, as well as the time to reach enteral feeds, will be reduced. Increasing the proportion of HM in enteral feeding should be considered a priority in the feeding strategy for preterm infants in clinical practice.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".