Hindmilk as a Rescue Therapy in Very Preterm Infants with Suboptimal Growth Velocity
Bibliographic record
Abstract
Despite advances in neonatal nutrition, very preterm infants remain at increased risk of extrauterine growth faltering. This prospective study aimed to examine the effect of hindmilk, the milk at the end of a breast expression session, on growth and plasma fatty acids (FAs) of infants born <30 weeks' gestation who had been on full enteral feeds for ≥2 weeks and had a weight gain of <15 g/kg/day despite optimizing energy and protein intakes. Weight and plasma FAs were assessed before and two weeks after feeding hindmilk. Growth anthropometrics were assessed weekly for four weeks. Paired t-tests and multiple linear regression were used for statistical analyses of data from 34 infants and their 29 mothers. There was a significant increase in weight gain in the two weeks after feeding hindmilk (MD 3.9, 95%CI 1.2-6.5 g/kg/day). Weight Z-scores were larger at two weeks (MD 0.61, 95%CI 0.02-1.20) and onwards. Head circumference Z-scores were larger at three weeks (MD 0.83, 95%CI 0.20-1.47) and onwards. Plasma linoleic acid (LA) and α-linolenic acid (ALA) increased after feeding hindmilk. In conclusion, hindmilk may improve weight and head growth and increase LA and ALA in very preterm infants with suboptimal growth. A large randomized controlled trial is required to examine and validate the potential benefits of hindmilk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".