1811 High-Flow Nasal Cannulae for Respiratory Support of Preterm Infants: A Review of the Evidence
Bibliographic record
Abstract
Background High-flow nasal cannulae (HFNC) are gaining in popularity as a form of non-invasive respiratory support for preterm infants in neonatal intensive care units around the world. They are proposed as an alternative to nasal continuous positive airway pressure (NCPAP) for treating respiratory distress in a variety of clinical situations, including post-extubation support, primary therapy from birth, and to ‘wean’ from NCPAP. Objectives To present and discuss the available evidence for the use of HFNC in various roles in the preterm population. Methods We performed an internet-based literature search for relevant, original research articles (both randomised and not) on the use of HFNC in preterm infants. Results 18 studies were included in the review. Distending pressure generated by HFNC in preterm infants increases with increasing flow rate and decreasing infant size, and may vary according to the amount of leak around the prongs. HFNC may be as effective as NCPAP at improving respiratory parameters such as tidal volume and work of breathing in preterm infants, but perhaps only at flow rates >2 Litres per minute. Based on available published evidence, the efficacy and safety of HFNC in preterm infants remain to be determined. Conclusions There is increasing evidence from clinical trials to support the use of HFNC treatment of preterm infants with respiratory failure, however uncertainty remains about efficacy, safety and optimal flow rates. Until the results of randomised trials in progress are known, widespread use of HFNC to treat preterm infants cannot be recommended.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".