B.7 Nutrition in the first two weeks of life, neonatal brain growth and cognitive outcomes in children born very preterm
Bibliographic record
Abstract
Background: Nutrition in early life plays a critical role in the growth and neurodevelopment of preterm neonates. However, whether early nutrition modified the association of white matter injury (WMI) with brain maturation and neurodevelopmental outcomes remains unclear. Methods: In this prospective cohort study, very preterm neonates were recruited from the NICU at BC Women’s Hospital. MRI and measures of NAA/choline were obtained. Energy intake was recorded over the first two weeks of life and the cohort was dichotomized. Neurodevelopmental outcomes were assessed at 4.5 years of age using WPPSI-III. Results: Neonates in the high lipid group had higher levels of NAA/choline in the basal ganglia. When accounting for confounders, this relationship was only significant in neonates without WMI (p=0.04). Overall, neonates with larger WMI volumes had lower IQ scores at 4.5 years (p<0.001). However, this relationship was attenuated in the high lipid group (p=0.002) relative to the lower lipid intake group. Conclusions: In this cohort, higher energy intake is associated with increased brain maturation. Similarly, neonates with large WMI had higher full-scale IQ if they received greater lipid intake in the neonatal period, suggesting that greater early lipid intake may contribute to blunting the deleterious effects of WMI on neurodevelopmental outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".