Accuracy of lung ultrasound in predicting extubation failure in neonates: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective To systematically review and meta‐analyze the diagnostic accuracy of lung ultrasound score (LUS) in predicting extubation failure in neonates. Study Design MEDLINE, COCHRANE, EMBASE, CINAHL, and clinicaltrials.gov were searched up to 30 November 2022, for studies evaluating the diagnostic accuracy of LUS in predicting extubation outcome in mechanically ventilated neonates. Methodology Two investigators independently assessed study eligibility, extracted data, and assessed study quality using the Quality Assessment for Studies of Diagnostic Accuracy 2 tool. We conducted a meta‐analysis of pooled diagnostic accuracy data using random‐effect models. Data were reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. We calculated pooled sensitivity and specificity, pooled diagnostic odds ratios with 95% confidence intervals (CI), and area under the curve (AUC). Results Eight observational studies involving 564 neonates were included, and the risk of bias was low in seven studies. The pooled sensitivity and specificity for LUS in predicting extubation failure in neonates were 0.82 (95% CI: 0.75−0.88) and 0.83 (95% CI: 0.78−0.86), respectively. The pooled diagnostic odds ratio was 21.24 (95% CI: 10.45−43.19), and the AUC for LUS predicting extubation failure was 0.87 (95% CI: 0.80−0.95). Heterogeneity among included studies was low, both graphically and by statistical criteria ( I 2 = 7.35%, p = 0.37). Conclusions The predictive value of LUS in neonatal extubation failure may hold promise. However, given the current level of evidence and the methodological heterogeneity observed, there is a clear need for large‐scale, well‐designed prospective studies that establish standardized protocols for lung ultrasound performance and scoring. Registration The protocol was registered in OSF ( https://doi.org/10.17605/OSF.IO/ZXQUT ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".