Impact of Donor Milk on Short- and Long-Term Growth of Very Low Birth Weight Infants
Bibliographic record
Abstract
Mother’s own milk (MOM) reduces the risk of morbidities in very low birth weight (VLBW) infants. When MOM is unavailable, donor breastmilk (DM) is used, with unclear impact on short- and long-term growth. This retrospective analysis compared anthropometric data at six time points from birth to 20–24 months corrected age in VLBW infants who received MOM supplements of preterm formula (n = 160) versus fortified DM (n = 161) during neonatal intensive care unit (NICU) hospitalization. The cohort was 46% female; mean birth weight and gestational age (GA) were 998 g and 27.3 weeks. Multilevel linear growth models assessed changes in growth z-scores short-term (to NICU discharge) and long-term (post-discharge), controlling for amount of DM or formula received in first 28 days of life, NICU length of stay (LOS), birth GA, and sex. Z-scores for weight and length decreased during hospitalization but increased for all parameters including head circumference post-discharge. Short-term growth was positively associated with LOS and birth GA. A higher preterm formula proportion, but not DM proportion, was associated with slower rates of decline in short-term growth trajectories, but feeding type was unrelated to long-term growth. In conclusion, controlling for total human milk fed, DM did not affect short- or long-term growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".