Supraglottic airway devices for administration of surfactant to newborn infants with respiratory distress syndrome: a narrative review
Bibliographic record
Abstract
Surfactant is an effective treatment for respiratory distress syndrome, being particularly important for infants in whom continuous positive airway pressure (CPAP) provides insufficient support. Supraglottic airway devices present an attractive option for surfactant delivery, particularly as an alternative to methods dependent on direct laryngoscopy, a procedural skill that is both difficult to learn and in which to maintain competence. Published studies provide encouraging data that surfactant administration by supraglottic airway device can be performed with a high rate of success and may reduce the need for subsequent intubation compared with either continued CPAP or surfactant administration via endotracheal tube. However, existing randomised controlled trials (RCTs) are heterogeneous in design and include just over 350 infants in total. To date, all RCT evidence has been generated in tertiary units, whereas the greatest potential for benefit from the use of these devices is likely to be in non-tertiary settings. Future research should investigate choice and utility of device in addition to safety and effectiveness of procedure. Importantly, studies conducted in non-tertiary settings should evaluate feasibility, meaningful clinical outcomes and the impact that this approach might have on infants and their families. Supraglottic airway devices may represent a simple and effective mode of surfactant administration that can be widely used by a variety of clinicians. However, further well-designed RCTs are required to determine their role, safety and effectiveness in both tertiary and non-tertiary settings before introduction into routine clinical practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".