Correlation Between Early Postnatal Body Weight Changes and Lung Ultrasound Scores as Predictors of Bronchopulmonary Dysplasia in Preterm Infants; A Secondary Analysis of a Prospective Study
Bibliographic record
Abstract
Abstract Purpose of the study: Recent research links Early Weight-Changes (EWC) with bronchopulmonary dysplasia (BPD) in preterm neonates, while lung ultrasound score (LUS) has shown promise in predicting BPD. We aimed to explore the correlation between LUS and EWC as markers of extravascular lung edema. Objective: To investigate the correlation between LUS and EWC in preterm infants with respiratory distress syndrome regarding future BPD development. Methods: This secondary analysis of a prospective study involved infants ≤ 28 weeks' gestation. Enrolled infants underwent lung ultrasound assessment on postnatal days 3, 7 and 14, measuring LUS. Respiratory severity score (RSS) and EWC were computed on the same time-points. Infants were classified as either having BPD or not. Descriptive statistics, Correlation Coefficient, and Area Under the Receiver Operating Characteristic Curve (AUROC) analysis were utilized. Results: Of 132 infants, 70 (53%) had BPD. Univariate analysis revealed statistically significant differences in LUS, EWC, and RSS at days 3, 7, and 14 between BPD and no-BPD groups (p < 0.001). A statistically significant but weak positive correlation existed between LUS and EWC at all three assessment points. AUROC analysis indicated LUS having superior predictive capacity for the need for invasive mechanical ventilation at day 14 as well as the later BPD development compared to EWC (p < 0.0001). Conclusions: In a cohort of extreme preterm infants, our study revealed a positive yet weak correlation between LUS and EWC, suggesting that EWC was not the major contributing to the evolving chronic lung disease.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".