Lung and diaphragm ultrasound as predictors of successful weaning from nasal continuous positive airway pressure in preterm infants
Bibliographic record
Abstract
OBJECTIVE: Assessment of the utility of lung and diaphragm ultrasound in the prediction of successful weaning from nasal continuous positive airway pressure (NCPAP) in preterm infants. STUDY DESIGN: This prospective cohort study was conducted on preterm infants who were considered ready for weaning off NCPAP. Lung and diaphragm ultrasound were performed just before and 3 h after weaning off NCPAP. The primary outcome was to evaluate the accuracy of lung ultrasound (LUS) in predicting successful weaning from NCPAP. RESULTS: Out of 65 enrolled preterm infants, 30 (46.2%) were successfully weaned from NCPAP to room air. The successful weaning group had higher gestational ages, lower incidences of previous invasive mechanical ventilation, and treated hemodynamically significant patent ductus arteriosus before the trial weaning. A LUS score of ≤6, measured before discontinuation of NCPAP, exhibited a predictive sensitivity of 80% and specificity of 75% for successful weaning (Area under the curve (AUC) = 0.865, ≤.001). When the LUS score was assessed 3 h after weaning from NCPAP, a cutoff point of ≤7 predicted successful weaning with a sensitivity and specificity of 90% and 60% respectively (AUC = 0.838, p ≤ .001). The diaphragmatic thickness fraction (DTF) was significantly lower in the successful weaning group. After adjustment for various factors, LUS score remained the only independent predictor of successful weaning. CONCLUSION: LUS score before weaning from NCPAP has a good sensitivity and specificity for predicting successful weaning from NCPAP in preterm infants. Diaphragmatic excursion and DTF were not good 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 | 0.001 | 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 | 0.001 | 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".