Does ventricular volume affect the neurodevelopmental outcome in infants with intraventricular hemorrhage?
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to investigate whether ventricular volume in posthemorrhagic ventricle dilatation impacts neurodevelopmental outcome. Infants were scanned with 3D cranial ultrasound in the first few months of life, and their neurodevelopmental outcome at 4, 8, 12, and 24 months corrected age (CA) was examined.\nMETHODS: Forty-nine infants who suffered an intraventricular hemorrhage (IVH) were enrolled in the study. Subjects were scanned multiple times during their stay in the neonatal intensive care unit. Images were manually segmented to measure total volume of the lateral ventricles, and the highest volume was taken. Infants with a volume ≤ 20 cc were considered low-volume (n = 33), and infants with a volume ≥ 40 cc were considered high-volume (n = 12). Developmental outcome was assessed at 4, 8, and 12 months CA with the Alberta Infant Motor Scale (AIMS) and Infant Neurological International Battery (Infanib), and at 24 months CA with the Bayley Scales of Infant Development 3e (BSID III).\nRESULTS: Infants in the low-volume group had higher scores on the Infanib at 4 months CA, and on both the AIMS and Infanib at 8 and 12 months CA, even after controlling for gestational age, birth weight, and worst grade of IVH. We observed a trend where low-volume infants also scored higher on the cognitive and gross-motor subtests of the BSID III at 24 months CA.\nCONCLUSION: Our results show that ventricular volume affects neurodevelopmental outcome in infants with IVH. This finding could guide the timing of future interventions, as earlier intervention may decrease the likelihood of adverse neurodevelopmental outcome.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".