Prediction of weaning readiness off nasal CPAP in preterm infants using point‐of‐care lung ultrasound
Bibliographic record
Abstract
This study's primary aim was to assess whether end-expiratory lung ultrasound severity score (expLUSsc) at Day 3 of life, the second week of life, and before weaning off nasal continuous positive airway pressure (nCPAP) can predict the weaning readiness off nCPAP trial in preterm infants. The secondary aim was to evaluate the value of adding lung tidal recruitment (LTR) to expLUSsc (expLUSsc-plus-LTR) to improve predictability. We conducted a prospective study on premature infants <33 weeks of gestation. Point-of-care lung ultrasound (POC-LUS) was performed on Day 3, the second week of life, before and after the trial off nCPAP. expLUSsc, pleural thickness, and LTR were assessed. A receiver operator curve was constructed to evaluate the ability of POC-LUS to predict the weaning readiness off nCPAP. A total of 148 studies were performed on 39 infants, of them 12 weaned off nCPAP from the first trial and 27 infants failed attempts off nCPAP. An expLUSsc cut-off 8 before the first trial of weaning off nCPAP has a sensitivity and specificity of 88% and 90%, and positive and negative predictive values of 87% and 92%, respectively, with area under the curve (AUC) was 0.87 (CI: 0.8-0.93), p < .0001. If LTR is added to an expLUSsc cut-off 8 (expLUSsc-plus-LTR) before the first trial of weaning, then sensitivity and specificity of 95% and 90%, and positive and negative predictive values of 88% and 90%, respectively, with AUC was 0.95 (CI: 0.91-0.99), p < .0001. In conclusion, this study demonstrated the ability of POC-LUS to predict the weaning readiness off nCPAP in premature infants. The use of this simple bedside noninvasive test can potentially avoid the exposure of premature infants to multiple unsuccessful weaning cycles.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".