A.07 Head circumference in preterm neonates: size at birth and postnatal growth predict neurodevelopment at 18 months
Bibliographic record
Abstract
Background: We determined the association between head circumference (HC) at birth and through neonatal intensive care with neurodevelopmental outcome in preterm neonates, accounting for brain injury on MRI. Methods: 169 neonates born 24-32 weeks gestation were studied prospectively with serial MRI. HC was measured at birth and discharge from neonatal intensive care. Outcome was assessed at 18 months corrected age using Bayley Scales of Infant & Toddler Development III motor and cognitive scores. Using multivariate linear regressions we evaluated the association between HC and outcomes, accounting for severity of brain injury and postnatal infection. Results: 46 neonates had HC <10th percentile at birth (SHC) which predicted poorer motor (~4 points; p=0.001) and cognitive (~4 points; p=0.005) outcomes, relative to those with normal HC at birth. In 9 of these neonates, SHC persisted to discharge; they had dramatically lower motor scores (15 points; p=0.004) and cognitive scores (12 points; p<0.001), even after adjusting for known risk factors Those born with SHC whose HC normalized by discharge did not show significantly poorer outcomes than those born with normal HC. Conclusions: The relationship between small HC at birth and adverse neurodevelopmental outcomes can be attenuated with normalization of head growth through the period of neonatal intensive care.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".