Extravascular lung water assessment by lung ultrasound in infants following pediatric cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Lung edema is a significant factor in prolonged mechanical ventilation and extubation failure after cardiac surgery. This study assessed the predictive capability of point-of-care Lung Ultrasound (LUS) for the duration of mechanical ventilation and extubation failure in infants following cardiac procedures. METHODS: We conducted a prospective observational trial on infants under 1 year, excluding those with pre-existing conditions or requiring extracorporeal membrane oxygenation. LUS was performed upon intensive care unit (ICU) admission and prior to extubation attempts. B-line density was scored by two independent observers. The primary outcomes included the duration of mechanical ventilation and extubation failure, the latter defined as the need for reintubation or non-invasive ventilation within 48 h post-extubation. RESULTS: The study included 42 infants, with findings indicating no correlation between initial LUS scores and extubation timing. Extubation failure occurred in 21% of the patients, with higher LUS scores observed in these cases (p = 0.046). However, interobserver variability was high, impacting the reliability of LUS scores to predict extubation readiness. CONCLUSIONS: LUS was ineffective in determining the length of postoperative ventilation and extubation readiness, highlighting the need for further research and enhanced training in LUS interpretation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".