Effects of synchronisation during SiPAP-generated nasal intermittent positive pressure ventilation (NIPPV) in preterm infants
Bibliographic record
Abstract
OBJECTIVE: The SiPAP flow driver (Care Fusion, Dublin, Ohio, USA) offers synchronised nasal intermittent positive pressure ventilation (sNIPPV) using an abdominal capsule. This study aims to describe the accuracy and effects of synchronised NIPPV using SiPAP in preterm infants. DESIGN: Ten infants, born <28 weeks' gestation, receiving synchronised SiPAP-generated NIPPV, in 'biphasic trigger' mode, were observed. Abdominal capsule signals, delivered pressures, respiratory pattern and oxygen saturations were recorded. Tidal volume (VT), apnoeas, proportion of breaths supported by SiPAP and time between inspiration onset and SiPAP pressure rise were analysed. RESULTS: Infants were of median 26(+0) weeks' gestational age and birth weight 776 g. Mean (SD) respiratory rate (RR) was 53 (14)/min. 82% (17) of spontaneous breaths triggered a SiPAP pressure peak. Mean time between inspiration and SiPAP pressure rise was 28 (20) ms. There was no difference in VT when breaths triggered a SiPAP pressure peak compared with breaths without a pressure peak. No VT was generated by pressure peaks delivered during apnoea. Capsule signals were not recognised following >10% of breaths, resulting in asynchronous NIPPV delivery. Movements resulted in irregular SiPAP pressures and desaturation. When the RR was faster, >55/min, breaths irregularly triggered a SiPAP pressure peak (p=0.003). Compared with times when every breath resulted in a pressure peak, lower mean pressures were achieved, 7.9 vs 8.4 cm H2O (p=0.02). CONCLUSIONS: The SiPAP synchronisation system triggered rapidly with most spontaneous breaths, but did not result in larger tidal volumes. When the RR was >55/min, the SiPAP delivered fewer pressure peaks at lower pressures.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".