Visual Development of Human Milk–Fed Preterm Infants Provided With Extra Energy and Nutrients After Hospital Discharge
Bibliographic record
Abstract
BACKGROUND: Human milk (HM) is the optimal way to nourish preterm low birth weight (LBW) infants after hospital discharge. However, there are few data on which to assess whether HM alone is sufficient to address hospital-acquired nutrition deficits, and no adequately powered studies have examined this question using neurodevelopment as an outcome. The purpose of this work was to determine whether adding extra energy and nutrients to the feedings of predominantly HM-fed LBW infants early after discharge improves their visual development. Visual development was used in this study as a surrogate marker for neurodevelopment. METHODS: At discharge, 39 predominantly HM-fed LBW infants (750-1800 g, 1288 ± 288 g) were randomized to receive human milk alone (control) or around half of the HM received daily mixed with a multinutrient fortifier (intervention) for 12 weeks. Grating acuity (ie, visual acuity) and contrast sensitivity were assessed using sweep visual-evoked potential tests at 4 and 6 months corrected age. RESULTS: At 4 and 6 months corrected age, intervention infants demonstrated higher grating acuity compared to those in the control group (intervention: 7.8 ± 1.3 and 9.7 ± 1.2 [cycles/degree] vs control 6.9 ± 1.2 and 8.2 ± 1.3, P = .02). Differences in contrast sensitivity did not reach statistical significance (P = .11). CONCLUSION: Adding a multinutrient fortifier to a portion of the expressed breast milk provided to predominantly HM-fed LBW infants early after discharge improves their early visual development. Whether these subtle differences in visual development apply to other aspects of development or longer term neurodevelopment are worthy of future investigation.
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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 | 0.000 |
| 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".