Slower Postnatal Growth Is Associated with Delayed Cerebral Cortical Maturation in Preterm Newborns
Bibliographic record
Abstract
Slower postnatal growth is an important predictor of adverse neurodevelopmental outcomes in infants born preterm. However, the relationship between postnatal growth and cortical development remains largely unknown. Therefore, we examined the association between neonatal growth and diffusion tensor imaging measures of microstructural cortical development in infants born very preterm. Participants were 95 neonates born between 24 and 32 weeks gestational age studied twice with diffusion tensor imaging: scan 1 at a median of 32.1 weeks (interquartile range, 30.4 to 33.6) and scan 2 at a median of 40.3 weeks (interquartile range, 38.7 to 42.7). Fractional anisotropy and eigenvalues were recorded from 15 anatomically defined cortical regions. Weight, head circumference, and length were recorded at birth and at the time of each scan. Growth between scans was examined in relation to diffusion tensor imaging measures at scans 1 and 2, accounting for gestational age, birth weight, sex, postmenstrual age, known brain injury (white matter injury, intraventricular hemorrhage, and cerebellar hemorrhage), and neonatal illness (patent ductus arteriosus, days intubated, infection, and necrotizing enterocolitis). Impaired weight, length, and head growth were associated with delayed microstructural development of the cortical gray matter (fractional anisotropy: P < 0.001), but not white matter (fractional anisotropy: P = 0.529), after accounting for prenatal growth, neonatal illness, and brain injury. Avoiding growth impairment during neonatal care may allow cortical development to proceed optimally and, ultimately, may provide an opportunity to reduce neurological disabilities related to preterm birth.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".