Accuracy of lung and diaphragm ultrasound in predicting successful extubation in extremely preterm infants: A prospective observational study
Bibliographic record
Abstract
OBJECTIVE: Chest ultrasound has emerged as a promising tool in predicting extubation readiness in adults and children, yet its utility in preterm infants is lacking. Our aim was to assess the utility of lung ultrasound severity score (LUSS) and diaphragmatic function in predicting extubation readiness in extremely preterm infants. STUDY DESIGN: In this prospective cohort study, preterm infants < 28 weeks gestational age (GA) who received invasive mechanical ventilation for ≥12 h were enrolled. Chest ultrasound was performed before extubation. The primary outcome was lung ultrasound accuracy for predicting successful extubation at 3 days. Descriptive statistics and logistic regression were done using SPSS version 22. RESULTS: We enrolled 45 infants, of whom 36 (80%) were successfully extubated. GA and postmenstrual age (PMA) at extubation were significantly higher in the successful group. The LUSS was significantly lower in the successful group compared to failed group (11.9 ± 3.2 vs. 19.1 ± 3.1 p < 0.001). The two groups had no statistically significant difference in diaphragmatic excursion or diaphragmatic thickness fraction. Logistic regression analysis controlling for GA and PMA at extubation showed LUSS was an independent predictor for successful extubation (odd ratio 0.46, 95% confidence interval [0.23-0.9], p = 0.02). The area under the receiver operating characteristic curve was 0.95 (p ˂ 0.001) for LUSS, and a cut-off value of ≥15 had 95% sensitivity and 85% specificity in detecting extubation failure. CONCLUSION: In extremely preterm infants, lung ultrasound has good accuracy for predicting successful extubation. However, diaphragmatic measurements were not reliable predictors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".