Early nutritional influences on brain regions related to processing speed in children born preterm: A secondary analysis of a randomized trial
Bibliographic record
Abstract
BACKGROUND: Processing speed is a foundational skill supporting intelligence and executive function, areas often delayed in preterm-born children. The impact of early-life nutrition on gray matter facilitating processing speed for this vulnerable population is unknown. METHODS: Magnetic resonance imaging and the Wechsler Preschool and Primary Scale of Intelligence-IV Processing Speed Index were acquired in forty 5-year-old children born preterm with very low birth weight. Macronutrient (grams per kilogram per day) and mother's milk (percentage of feeds) intakes were prospectively collected in the first postnatal month and associations between early-life nutrition and the primary outcome of brain regions supporting processing speed were investigated. RESULTS: Children had a mean (SD) gestational age of 27.8 (1.8) weeks and 45% were male. Macronutrient intakes were unrelated, but mother's milk was positively related, to greater volumes in brain regions, including total cortical gray matter, cingulate gyri, and occipital gyri. CONCLUSION: First postnatal month macronutrient intakes showed no association, but mother's milk was positively associated, with volumetric measures of total and regional cortical gray matter related to processing speed in preterm-born children. This exploratory analysis suggests early-life mother's milk supports processing speed by impacting structural underpinnings. Further research is needed on this potential strategy to improve preterm 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".