Comparison of the effects of two human milk fortifiers with different energy sources on the body composition of premature infants
Bibliographic record
Abstract
Human milk fortification is recommended to meet the nutritional requirements of preterm infants. Most human milk fortifiers (HMFs) contain non-protein energy (NPE) predominantly as carbohydrate which may lead to high fat deposition relative to lean mass accretion. We hypothesized that fortifying human milk with a HMF containing NPE predominantly as fat (fatHMF) would result in a higher (1) lean mass accretion (percent lean mass) and (2) growth (anthropometry), compared to fortifying with an isocaloric, isonitrogenous HMF containing NPE predominantly as carbohydrate (carbHMF). In a double-blind randomized trial, 29 infants (≤32 weeks and appropriate for gestational age) admitted to the Neonatal Intensive Care Unit received either mother's milk fortified with the fatHMF (n = 14) or the carbHMF (n = 15). Body composition and growth measurements were performed at Baseline (at ≤10% of goal intake 150 ml/kg), Phase 1, and Phase 2 (3 weeks and 6 weeks, respectively, from starting HMF). Although neither percent lean (fat) mass nor growth were statistically different, by Phase 2 infants receiving fatHMF showed a 63% increase in percent fat mass, gained 1194 g in weight and 8.8 cm in length, whereas the carbHMF showed a 96% increase in percent fat mass, gained 1005 g in weight and 6.9 cm in length (p = 0.3586, 0.3815, and 0.1851 respectively). By Phase 2, the fatHMF infants gained 128 g in absolute dry lean tissue, whereas the carbHMF infants gained 99 g (p = 0.0362, Post hoc analysis). Differences of this magnitude are clinically important, but a larger study is required to demonstrate statistical significance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".