Early‐life nutrition is associated with processing speed at age 5 in children born preterm with very low birth weight
Bibliographic record
Abstract
OBJECTIVE: Processing speed is suboptimal among preterm-born children which is of concern as it is a foundational skill supporting higher-level cognitive functions. The study objective was to evaluate associations between early-life nutrition and processing speed in childhood. METHODS: Macronutrient and human milk (mother's own, donor) intakes from 137 children born preterm with very low birth weight enrolled in a nutrition feeding trial were included. Processing speed was evaluated at age 5 using the Wechsler Preschool and Primary Scale of Intelligence-fourth edition Processing Speed Index. Associations between early-life nutrition and processing speed were explored through linear regression. RESULTS: Children had a mean (standard deviation [SD]) birth gestational age of 28.1 (2.5) weeks, weight of 1036 (260) g and 52% were male. The mean (SD) assessment age was 5.7 (0.2) years. Sex-dependent relationships were identified between first postnatal month protein, lipid and energy intakes and processing speed at 5 years. For females, lower protein (per 0.1 g/kg/d: -0.88, 95% confidence interval [CI]: -1.53, -0.23; p = 0.01) and energy (per 10 kcal/kg/d: -2.38, 95% CI: -4.70, -0.05; p = 0.03) intakes were related to higher processing speed scores. Mother's milk provision was positively associated (per 10% increase: 0.80, 95% CI: 0.22, 1.37; p = 0.01) and donor milk was negatively associated (per 10% increase: -1.15, 95% CI: -2.22, -0.08; p = 0.04) with processing speed scores; no sex differences were observed. CONCLUSIONS: First postnatal month nutrition was related to processing speed at age 5 in children born preterm with very low birth weight. Early-life nutrition that supports processing speed may be leveraged to improve later cognitive outcomes for this vulnerable population.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".