New modalities to deliver surfactant in premature infants: a systematic review and meta-analysis
Bibliographic record
Abstract
CONTEXT: Surfactant is the principle treatment of respiratory distress syndrome, but the ideal method of its administration remains controversial. The intubation, surfactant administration and extubation (InSurE) method is proven to work but is invasive. The objective of this systematic review is to evaluate the efficacy and safety of the modalities of surfactant administration. METHODS: We searched MEDLINE, EMBASE and CENTRAL (inception to December 2015) for randomized trials comparing new modalities with InSurE method. The primary outcome was mortality and development of bronchopulmonary dysplasia (BPD). RESULTS: We screened 1837 citations and identified five unique trials were included; all were of unclear risk of bias. Four trials (400 infants) compared endotracheal catheters with InSurE, and one trial (70 infants) compared laryngeal masks (LMA) with InSurE. There was no significant difference between using endotracheal catheters compared with InSurE regarding infant mortality (risk ratio 1.05, 95% CI 0.57-1.94, 4 trials, 400 patients, p 0.87, I(2) 0%) or BPD (risk ratio 0.73, 95% CI 0.43-1.21, 4 trials, 400 patients, p 0.22, I(2) 0%). Adverse events were under-reported. CONCLUSION: The use of endotracheal catheters may provide comparable results to the InSurE method. There is limited evidence on the comparative efficacy of LMA.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".