Tiny Lungs, Big Decisions: A Meta‐Analysis Comparing Minimally Invasive Surfactant Therapy Versus Intubation–Surfactant–Extubation in Preterm Neonates With Respiratory Distress Syndrome
Bibliographic record
Abstract
Background: Neonatal respiratory distress syndrome (NRDS) is a leading cause of morbidity and mortality in preterm infants. Minimally invasive surfactant therapy (MIST) has emerged as a promising alternative to traditional approaches, aiming to reduce mechanical ventilation while maintaining spontaneous breathing. This meta‐analysis compares the efficacy and safety of MIST versus the intubation–surfactant–extubation (INSURE) method in preterm neonates with NRDS. Methods: We searched PubMed, Scopus, and Embase for eligible studies. Two independent reviewers screened studies via the Rayyan platform. We included randomized controlled trials and observational cohorts of preterm neonates (< 37 weeks) with NRDS requiring surfactant therapy, comparing MIST and INSURE. Data extraction included study characteristics, demographics, and clinical outcomes. Risk of bias was assessed using the Newcastle–Ottawa Scale and Cochrane tools. Risk ratios (RRs) and mean differences (MDs) with 95% confidence intervals (CIs) were calculated using a random‐effects model. Heterogeneity was evaluated via I 2 statistics. Results: Then, 17 studies ( n = 1931 neonates; MIST: 913, INSURE: 932) were included. Baseline characteristics were similar between groups. Mortality did not differ significantly (RR 0.62; 95% CI 0.38–1.01; p = 0.05). MIST was associated with reduced risks of bronchopulmonary dysplasia (RR 0.59; 95% CI 0.45–0.76), intraventricular hemorrhage (RR 0.66; 95% CI 0.48–0.92), patent ductus arteriosus (RR 0.75; 95% CI 0.61–0.93), and pneumothorax (RR 0.54; 95% CI 0.34–0.87). Rates of pulmonary hemorrhage and surfactant reflux were comparable. MIST also resulted in shorter oxygen day requirements (MD −2.45; p = 0.04) and need for mechanical ventilation (RR 0.54; p = 0.002). Duration of ventilation and NICU showed no significant differences. Conclusion: MIST proved to be a safer and more effective alternative to INSURE in preterm infants with NRDS, reducing several complications and mechanical ventilation needs. These findings highlight MIST’s potential as a preferred approach, warranting further research to support broader implementation.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".