High‐flow nasal cannulae are associated with increased diaphragm activation compared with nasal continuous positive airway pressure in preterm infants
Bibliographic record
Abstract
AIM: High-flow nasal cannulae (HFNC) are increasingly used for respiratory management of preterm infants. However, their ability to provide support compared to nasal continuous positive airway pressure (CPAP) has been questioned. We compared the effect of HFNC versus nasal CPAP on diaphragm electrical activity (EAdi) in preterm infants. METHODS: Preterm infants ≤1500 g were randomised in a crossover design to receive 2 hours of either Infant Flow(®) CPAP (IF-CPAP) at 5-6 cmH2 O or HFNC with the flow rate adjusted to achieve an equivalent pharyngeal pressure. A feeding catheter with miniaturised sensors was inserted for continuous EAdi measurement. RESULTS: The study comprised ten infants. Physiologic parameters and oxygen requirements were not different between the two modes. However, seven infants demonstrated a higher EAdi peak and six showed a higher EAdi tonic on HFNC, even though the mean group data showed no difference between HFNC and IF-CPAP. Neural inspiratory time was significantly longer with HFNC than IF-CPAP (0.55 ± 0.11 versus 0.48 ± 0.06 seconds, p = 0.018). CONCLUSION: In this cohort of preterm infants, the majority exhibited greater diaphragm activation, as assessed by neural breathing patterns, when supported with HFNC than IF-CPAP, suggesting that nasal CPAP may provide more effective respiratory support.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| 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".