Meta-Analysis of Lung Ultrasound Scores for Early Prediction of Bronchopulmonary Dysplasia
Bibliographic record
Abstract
Abstract Rationale Lung ultrasound scores (LUS) might be useful in monitoring neonates with chronic pulmonary insufficiency of prematurity and in predicting bronchopulmonary dysplasia (BPD). Given their ease of use, accuracy, and lack of invasiveness, LUS have been the subject of several recent studies. Objectives We sought to clarify whether LUS provide an accurate and early (within the first 2 wk of life) prediction of BPD in preterm infants of gestational age ⩽32 weeks. Methods This was a systematic review and diagnostic accuracy meta-analysis following PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols), PRISMA (Preferred Reporting Items for Systematic review and Meta-Analysis), and QUADAS (QUAlity of Diagnostic Accuracy Studies) guidelines. Studies designed to predict BPD in the first 2 weeks of life using LUS were selected. A classical LUS (calculated for 6 chest areas) and its extended version (eLUS, 10 chest areas) were tested. Results Seven studies (1,027 neonates) were meta-analyzed. LUS and eLUS showed good diagnostic accuracy in predicting BPD at 7 and 14 days of life (area under the curve, 0.85–0.87; pooled sensitivity, 70–80%; pooled specificity, 80–87%). The diagnostic accuracy of LUS and eLUS did not differ at any time point (area under the curve difference always P > 0.05). Repeating the analyses without outliers or with moderate to severe BPD as the outcome yielded similar results. Meta-regressions showed that prenatal steroid prophylaxis and sex were not significant effect confounders. Conclusions LUS are accurate for early prediction of BPD and moderate to severe BPD, in an average population of preterm infants ⩽32 weeks’ gestation. The diagnostic accuracy is similar for LUS and eLUS, so the use of the simpler score should be advocated. Registration: PROSPERO CRD42021233010
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.056 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".