Energy and Fat Intake for Preterm Infants Fed Donor Milk Is Significantly Impacted by Enteral Feeding Method
Bibliographic record
Abstract
BACKGROUND: Pasteurized donor milk is the recommended supplement for preterm infants when mother's milk volumes are insufficient. Compared with mother's milk, the macronutrient content of donor milk is thought to be lower due to pasteurization and additional container changes during processing. Given that poor growth is concerning for preterm infants, it is important to understand how processing and feeding methods influence the nutrition composition of donor milk feeds. The research aim of this study was to determine the effects of pasteurization and feeding method on the macronutrient and energy composition of donor milk. Ten donor milk pools were pasteurized, prepared according to neonatal practices, and infused through nasogastric tubes to simulate 4 feeding methods: bolus, 30 minutes, 60 minutes, and continuous feeding over 4 hours. Macronutrient concentrations were assessed after pasteurization, preparation, and each feeding method using a mid-infrared human milk analyzer. RESULTS: There were no significant decreases in macronutrient content after pasteurization or bolus feeding. However, energy and fat losses increased with slower infusion rates. After continuous feeding for 4 hours, energy and fat concentrations decreased by a mean of 17.3 (15.8-18.8) kcal/dL and 2.08 (1.90-2.25) g/dL (P < .0001), respectively. CONCLUSIONS: Pasteurization did not significantly reduce donor milk macronutrient and energy content; however, feeding method significantly impacted the final delivery of energy and fat.
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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 | 0.002 |
| 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 | 0.000 |
| 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".