Growth and Nutrient Intakes of Human Milk–Fed Preterm Infants Provided With Extra Energy and Nutrients After Hospital Discharge
Bibliographic record
Abstract
OBJECTIVES: The purpose of this pilot study was to determine whether mixing a multinutrient fortifier to approximately one half of the human milk fed each day for a finite period after discharge improves the nutrient intake and growth of predominantly human milk-fed low birth weight infants. We also assessed the impact of this intervention on the exclusivity of human milk feeding. METHODS: Human milk-fed (> or = 80% feeding per day) low birth weight (750-1800 g) infants (n = 39) were randomly assigned at hospital discharge to either a control or an intervention group. Infants in the control group were discharged from the hospital on unfortified human milk. Nutrient enrichment of human milk in the intervention group was achieved by mixing approximately one half of the human milk provided each day with a powdered multinutrient human milk fortifier for 12 weeks after discharge. Milk with added nutrients was estimated to contain approximately 80 kcal (336 kJ) and 2.2 g protein/100 mL plus other nutrients. Intensive lactation support was provided to both groups. RESULTS: Infants in the intervention group were longer during the study period, and those born < or = 1250 g had larger head circumferences than infants in the control group. There was a trend toward infants in the intervention group to be heavier at the end of the intervention compared with those in the control group. Mean protein, zinc, calcium, phosphorus, and vitamins A and D intakes were higher in the intervention group. CONCLUSIONS: Results from this study suggest that adding a multinutrient fortifier to approximately one half of the milk provided to predominantly human milk-fed infants for 12 weeks after hospital discharge may be an effective strategy in addressing early discharge nutrient deficits and poor growth without unduly influencing human milk feeding when intensive lactation support is provided.
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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) | 0.001 | 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 | 0.001 |
| 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".