Improving the quality of donor human milk to take advantage of more of the health benefits of mother's own milk composition
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pasteurized donor human milk (PDHM) is the recommended supplement when there is inadequate volume of mother's own milk (MOM) for very low birth weight (<1500 g, VLBW) infants. Differences in the composition of these milks may impact growth, morbidities or long-term development of infants. The aim of this review is to highlight current trends in understanding compositional differences between MOM and PDHM, technological advances in processing PDHM, and infant outcomes when VLBW infants are fed these milks. RECENT FINDINGS: Reported differences in the composition between MOM and PDHM are due to several factors including when and how milk is collected, sampled for analysis, and processed. Systematic reviews and primary research studies demonstrate that PDHM reduces the risk of necrotizing enterocolitis in VLBW infants but is also associated with slower postnatal growth. Work is ongoing to determine if alternative approaches to processing PDHM can improve milk composition and thereby infant growth and neurodevelopment and reduce morbidity. SUMMARY: PDHM is a key component of feeding VLBW infants when there is inadequate volume of MOM. Recent developments aim to optimize this source of nutrition and bioactive compounds for VLBW infants while further understanding limitations of its use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".