Prediction of Extubation Readiness Using Transthoracic Ultrasound in Preterm Infants
Bibliographic record
Abstract
ABSTRACT Objective To test the hypothesis that a lung ultrasound severity score (LUSsc) and assessment of left ventricular eccentricity index of the interventricular septum (LVEI) by focused heart ultrasound can predict extubation success in mechanically ventilated preterm infants with respiratory distress syndrome (RDS). Design Prospective observational study of premature infants <34 weeks’ of gestation age supported with mechanical ventilation due to RDS. LUSsc and LVEI were performed on postnatal days 3 and 7 by an investigator who was masked to infants’ ventilator parameters and clinical conditions. RDS was classified based on LUSsc into mild (score 0–9) and moderate-severe (score 10–18). A receiver operator curve was constructed to assess the ability to predict extubation success. Pearson’s correlation was performed between LVEI and pulmonary artery pressure (PAP). Setting Level III neonatal intensive care unit, Cairo, Egypt. Results A total of 104 studies were performed to 66 infants; of them 39 had mild RDS (LUSsc 0–9) and 65 had moderate-severe RDS (score ≥10). LUSsc predicted extubation success with a sensitivity and a specificity of 91% and 69%; the positive and negative predictive values were 61% and 94%, respectively. Area under the curve (AUC) was 0.83 (CI: 0.75-0.91). LVEI did not differ between infants that succeeded and failed extubation. However, it correlated with pulmonary artery pressure during both systole (r=0.62) and diastole (r=0.53) and with hemodynamically significant patent ductus arteriosus (r=0.27 and r=0.46, respectively). Conclusion LUSsc predicts extubation success in preterm infants with RDS whereas LVEI correlates with high PAP.
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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 | 0.001 | 0.008 |
| 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 | 0.000 | 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".