Punctate White Matter Abnormality in Moderate‐to‐Late Preterm Infants
Bibliographic record
Abstract
OBJECTIVE: Moderate-to-late preterm (MLP) infants contribute to the greatest proportion of preterm children with neurodevelopmental impairments. White matter injury (WMI) is common and predicts adverse outcomes in very preterm (VP) infants. However, little is known about white matter abnormality (WMA) in MLP infants. We investigated the burden and distribution of WMA in MLP infants. METHODS: MLP infants were recruited from a randomized trial on neonatal nutrition and a prospective observational cohort in New Zealand, and underwent brain magnetic resonance imaging (MRI) soon after birth and at term-equivalent age (TEA). WMA was manually segmented using an established method. Total and regional WMA volumes and percentage of WMA to total cerebral volume were calculated. Probabilistic WMA maps were generated and compared with WMI in VP infants and term infants with congenital heart disease. RESULTS: in WMA volume from early-life to term. Infants with and without WMA had mostly comparable pregnancy and neonatal characteristics. Probabilistic maps demonstrated a characteristic WMA topology, with most lesions in posterior followed by central and anterior regions. Trigonal areas were vulnerable across neonatal populations. INTERPRETATION: WMA is much more common in MLP infants than previously reported and occurs in a characteristic topology. WMA may be missed on TEA MRI, and its relationship with outcomes in MLP infants warrants attention. ANN NEUROL 2025;98:329-340.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".