Size and Location of Preterm Brain Injury and Associations With Neurodevelopmental Outcomes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We examined associations of white matter injury (WMI) and periventricular hemorrhagic infarction (PVHI) volume and location with 18-month neurodevelopment in very preterm infants. METHODS: A total of 254 infants born <32 weeks' gestational age were prospectively recruited across 3 tertiary neonatal intensive care units (NICUs). Infants underwent early-life (median 33.1 weeks) and/or term-equivalent-age (median 41.9 weeks) MRI. WMI and PVHI were manually segmented for quantification in 92 infants. Highest maternal education level was included as a marker of socioeconomic status and was defined as group 1 = primary/secondary school; group 2 = undergraduate degree; and group 3 = postgraduate degree. Eighteen-month neurodevelopmental assessments were completed with Bayley Scales of Infant and Toddler Development, Third Edition. Adverse outcomes were defined as a score of less than 85 points. Multivariable linear regression models were used to examine associations of brain injury (WMI and PVHI) volume with neurodevelopmental outcomes. Voxel-wise lesion symptom maps were developed to assess relationships between brain injury location and neurodevelopmental outcomes. RESULTS: = 0.03). In voxel-wise lesion symptom maps, brain injury involving the central and parietal white matter was associated with an increased risk of poorer motor outcomes. DISCUSSION: We found that brain injury volume and location were significant predictors of motor, but not cognitive outcomes, suggesting that different pathways may mediate outcomes across domains of neurodevelopment in preterm infants. Specifically, assessing lesion size and location may allow for more accurate identification of infants with brain injury at highest risk of poorer motor outcomes. These data also highlight the importance of socioeconomic status in cognitive outcomes, even in preterm infants with brain injury.
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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 | 0.004 |
| 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) | 0.002 | 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".