High-Flow Nasal Cannulae in the Management of Apnea of Prematurity: A Comparison With Conventional Nasal Continuous Positive Airway Pressure
Bibliographic record
Abstract
Apnea of prematurity (AOP) is frequently managed with nasal continuous positive airway pressure (NCPAP). Nasal cannula (NC) are used at low flows (<0.5 L/min) to deliver supplemental oxygen to neonates. A number of centers use high-flow nasal cannula (HFNC) in the management of AOP without measuring the positive distending pressure (PDP) generated. Objective. To determine the NC flow required to generate PDP equal to that provided by NCPAP at 6 cm H(2)O and to assess the effectiveness of HFNC as compared NCPAP in the management of AOP. Method. Forty premature infants, gestation 28.7 +/- 0.4 weeks (mean +/- standard error of mean), postconceptual age at study 30.3 +/- 0.6 weeks, birth weight 1256 +/- 66 g, study weight 1260 +/- 63 g who were being managed with conventional NCPAP for at least 24 hours for clinically significant apnea of prematurity, were enrolled in a trial of ventilator-generated conventional NCPAP versus infant NC at flows of up to 2.5 L/min. End expiratory esophageal pressure was measured on NCPAP and on NC, and the gas flow on NC was adjusted to generate an end expiratory esophageal pressure equal to that measured on NCPAP. Two 6-hour periods were continuously recorded and the data were stored on computer. Results. The flow required to generate a comparable PDP with NC varied with the infant's weight and was represented by the equation: flow (L/min) = 0.92 + 0.68x, x = weight in kg, R = 0.72. There was no difference in the frequency and duration of apnea, bradycardia or desaturation per recording between the 2 systems. Conclusion. NC at flows of 1 to 2.5 L/min can deliver PDP in premature neonates. HFNC is as effective as NCPAP in the management of AOP.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".