A Lung Ultrasound Severity Score Predicts Chronic Lung Disease in Preterm Infants
Bibliographic record
Abstract
Objective To test the hypothesis that a lung ultrasound severity score (LUSsc) can predict the development of chronic lung disease (CLD) in preterm neonates. Study Design Preterm infants <30 weeks' gestational age were enrolled in this study. Lung ultrasound (LUS) was performed between 1 and 9 postnatal weeks. All ultrasound studies were done assessing three lung zones on each lung. Each zone was given a score between 0 and 3. A receiver operating characteristic curve was constructed to assess the ability of LUSsc to predict CLD. Results We studied 27 infants at a median (interquartile range [IQR]) gestational age and birth weight of 26 weeks (25–29) and 780 g (530–1,045), respectively. Median (IQR) postnatal age at the time of LUS studies was 5 (2–8) weeks. Fourteen infants who developed CLD underwent 34 studies. Thirteen infants without CLD underwent 30 studies. Those who developed CLD had a higher LUSsc than those who did not (median [IQR] of scores: 9 [6–12] vs. 3 [1–4], p < 0.0001). An LUSsc cutoff of 6 has a sensitivity and specificity of 76 and 97% and positive and negative predictive values of 95 and 82%, respectively. Adding gestational age < 27 weeks improved sensitivity and specificity to 86 and 98% and positive and negative predictive values to 97 and 88%. Conclusion LUSsc between 2 and 8 weeks can predict development of CLD in preterm neonates.
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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 | 0.010 |
| 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 | 0.001 | 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".