The impact of breast milk feeding on early brain development in preterm infants in China: An observational study
Bibliographic record
Abstract
BACKGROUND: The prevailing consensus from large epidemiological studies is that breastfeeding is associated with improved IQ and cognitive functioning in later childhood and adolescence. Current research is exploring the association between breastfeeding and early brain development in preterm infants. OBJECTIVE: To explore the differences in brain gray matter between breastmilk-fed and formula-fed preterm infants using structural and functional magnetic resonance imaging. METHODS: A convenience sample of breastmilk-fed preterm infants(n = 34) and formula-fed infants (n = 22) aged approximately 32 weeks. At near term-equivalent age, MR scanning was performed. Gray matter structural and functional differences between the two groups were assessed using MATLAB software for voxel-based morphometry (VBM) and amplitude of low-frequency fluctuation (ALFF) analysis. RESULTS: Maternal and neonatal demographic characteristics showed no significant difference between the two groups. Breastmilk-fed infants had greater regional gray matter volume on MRI than formula-fed infants in multiple brain regions, including the bilateral frontal lobe (BA11, BA46), right temporal lobe (BA37), and left caudate nucleus, at a statistical threshold of p<0.01 (AlphaSim corrected) with a cluster size of >40 voxels. Compared with formula-fed infants, breastmilk-fed infants showed increased brain activation on fMRI in the right superior temporal gyrus (BA41). CONCLUSION: Breastmilk-fed infants had greater regional gray matter development and increased regional gray matter function compared with formula-fed infants at near term-equivalent age, suggesting breastmilk feeding in the early period after birth may have some degree of influence on early brain development in preterm infants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".