Higher Energy, Lipid, and Carbohydrate Provision to Very Low‐Birth‐Weight Infants Is Differentially Associated With Neurodevelopment at 18 Months, Despite Consistent Improvements in Weight Gain
Bibliographic record
Abstract
BACKGROUND: The impact of suboptimal intakes on neurodevelopment of very low-birth-weight (VLBW, <1500 g) infants, particularly those born small for gestational age, <26 weeks, <1000 g, or with morbidities is not well defined. We investigated how macronutrient/energy intakes are associated with growth and neurodevelopment among VLBW infants, adjusted for the aforementioned vulnerabilities. Our hypothesis was that higher nutrient intakes would be positively associated with weight gain and neurodevelopment. METHODS: weeks were collected prospectively from VLBW infants (n = 302) enrolled in a previous trial (ISRCTN35317141). Neurodevelopment was assessed by the Bayley-III at 18 months' corrected gestational age. Relationships between quartiles of macronutrient/energy intakes, growth, and neurodevelopment were assessed. RESULTS: Infants born <1000 g, <26 weeks, or with morbidities had lower nutrient intakes and slower growth than infants born ≥1000 g, ≥26 weeks, or with no morbidities, respectively (P < 0.05). Higher quartiles of energy, lipid, and carbohydrate intakes were positively associated with growth velocity (P = <0.0001-0.007); no association was observed for protein intake. Energy, protein-to-energy ratio and lipid intakes were associated with cognitive scores (P = 0.001-0.004); however, intakes within the second and third quartiles were generally associated with the highest cognitive scores. No nutrient intakes were associated with language or motor scores across the entire study period. CONCLUSION: Smaller, more immature VLBW infants and those with morbidity have the greatest risk of poor nutrition and growth. Increasing macronutrient/energy intakes are generally associated with improved weight gain, but not necessarily improved neurodevelopment.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".