Are high flow nasal cannulae noisier than bubble CPAP for preterm infants?
Bibliographic record
Abstract
BACKGROUND: Noise exposure in the neonatal intensive care unit is believed to be a risk factor for hearing loss in preterm neonates. Continuous positive airway pressure (CPAP) devices exceed recommended noise levels. High flow nasal cannulae (HFNC) are an increasingly popular alternative to CPAP for treating preterm infants, but there are no in vivo studies assessing noise production by HFNC. OBJECTIVE: To study whether HFNC are noisier than bubble CPAP (BCPAP) for preterm infants. METHODS: An observational study of preterm infants receiving HFNC or BCPAP. Noise levels within the external auditory meatus (EAM) were measured using a microphone probe tube connected to a calibrated digital dosimeter. Noise was measured across a range of frequencies and reported as decibels A-weighted (dBA). RESULTS: A total of 21 HFNC and 13 BCPAP noise measurements were performed in 21 infants. HFNC gas flows were 2-5 L/min, and BCPAP gas flows were 6-10 L/min with set pressures of 5-7 cm of water. There was no evidence of a difference in average noise levels measured at the EAM: mean difference (95% CI) of -1.6 (-4.0 to 0.9) dBA for HFNC compared to BCPAP. At low frequency (500 Hz), HFNC was mean (95% CI) 3.0 (0.3 to 5.7) dBA quieter than BCPAP. Noise increased with increasing BCPAP gas flow (p=0.007), but not with increasing set pressure. There was a trend to noise increasing with increasing HFNC gas flows. CONCLUSIONS: At the gas flows studied, HFNC are not noisier than BCPAP for preterm infants.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".