Integrating Clinical and Neuroimaging Markers to Predict the Onset of Posthemorrhagic Ventricular Dilatation in Preterm Neonates
Bibliographic record
Abstract
BACKGROUND: Posthemorrhagic ventricular dilatation (PHVD) is a major complication of intraventricular hemorrhage (IVH); it is associated with high risks of cerebral palsy and cognitive deficits compared with infants without PHVD. This study aims to explore the early perinatal risk factors-associated with the risk of progressive PHVD. METHODS: Neonates ≤29 weeks gestational age (GA) with Grade II-III IVH and periventricular hemorrhagic infarct (PVHI) between 2015 and 2021 were retrospectively reviewed. All cranial ultrasounds done within 14 days postnatal age (PNA) were assessed for grade of IVH, anterior horn width (AHW), ventricular index (VI), and thalamo-occipital index (TOD). The outcome was defined as death of any cause or VI and/or AHW and/or TOD ≥ moderate-risk zone based on an ultrasound done beyond two weeks PNA. RESULTS: A total of 146 infants with a mean GA of 26 ± 1.8 weeks, birth weight 900 ± 234 g were included, 46% were females. The primary outcome occurred in 56 (39%) infants; among them 17 (30%) and 11 (20%) needed ventricular reservoir and shunt insertion, respectively. The risk factors present within 14 days PNA that significantly increased the odds of developing PHVD were hemodynamically significant patent ductus arteriosus (odds ratio [OR] 6.1, 95% confidence interval [CI] 1.9 to 22), culture-proven sepsis (OR 5.4, 95% CI 1.8 to 18), Grade III IVH (OR 4.6, 95% CI 1.1 to 22), PVHI (OR 3.0, 95% CI 0.9 to 10), and VI (OR 2.1, 95% CI 1.6 to 2.9). CONCLUSIONS: Clinical predictors such as significant ductus arteriosus and bacterial septicemia, along with risk levels of AHW and VI measured with early cranial ultrasounds, are potential predictors of subsequent onset of PHVD.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".