Nasal High-Frequency Oscillation for Lung Carbon Dioxide Clearance in the Newborn
Bibliographic record
Abstract
BACKGROUND: Noninvasive ventilation has been used increasingly in recent years to minimize the duration of endotracheal mechanical ventilation in neonates due to its association with lung injury. Nasal high-frequency oscillation (nHFO) is a relatively new noninvasive modality but evidence for its use is limited. OBJECTIVE: The goal of this study was to compare the CO2 clearance efficacy of nHFO and noninvasive positive pressure ventilation (NIPPV) in a neonatal lung model. DESIGN/METHODS: A newborn mannequin with dimensions and anatomy similar to a term infant was utilized. It was connected to a commercially available neonatal mechanical ventilator using a manufacturer-provided nasal adaptor. Various modes of noninvasive ventilation were compared as CO2 clearance was measured at the oropharynx by an end-tidal CO2 analyzer following the addition of a known amount of CO2 into the lung. Measurements were obtained at two different lung compliances using nHFO and compared with nCMV and nasal continuous positive airway pressure (nCPAP) as a control. Pressures near the nasal adaptor and the larynx were simultaneously measured with in-line pressure transducers. RESULTS: Whereas no CO2 elimination was observed under nCPAP, its clearance with nHFO was 3-fold greater as compared to NIPPV. On nHFO, CO2 clearance was inversely proportional to frequency and maximal at 6 and 8 Hz. At a lower lung compliance, CO2 clearance was significantly higher at 6 Hz as compared to 10 Hz. During nHFO set to deliver a MAP of 10.0, we documented pressures of 7.2 ± 0.3 at the nasal adaptor and only 2.3 ± 0.3 cm H2O at the larynx. CONCLUSIONS: Nasal HFO is effective and superior to NIPPV at lung CO2 elimination in a newborn mannequin model. The use of nHFO as the preferred mode of noninvasive ventilation warrants further clinical studies.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".