Continuous neurally adjusted ventilation: a feasibility study in preterm infants
Bibliographic record
Abstract
OBJECTIVES: To assess the feasibility and tolerance of NeuroPAP, a new non-invasive ventilation mode which continuously adjusts (during both inspiration and expiration) the pressure support proportionally to the diaphragm electrical activity (Edi), in preterm infants and to evaluate the impact on ventilation pressure and Edi. DESIGN: Prospective cross-over single-centre feasibility study. SETTING: One level 3 neonatal intensive care unit in Canada. PATIENTS: Stable preterm infants ventilated with non-invasive positive pressure ventilation (NIPPV). INTERVENTIONS: 0 (60 min), in continuous positive airway pressure (15 min) and again in NIPPV (30 min). Main outcome measures included tolerance, ventilation pressure, Edi and patient-ventilator synchrony. RESULTS: Twenty infants born at 28.0±1.0 weeks were included. NeuroPAP was well tolerated and could be delivered during 100% of planned period. During NeuroPAP, the PEEP was continuously adjusted proportionally to tonic diaphragm Edi, although the average PEEP value was similar to the set minimal pressure. During NeuroPAP, 83 (78-86)% breaths were well synchronised vs 9 (6-12)% breaths during NIPPV (p<0.001). CONCLUSIONS: NeuroPAP is feasible and well tolerated in stable preterm infants, and it allows transient adaptation in PEEP in response to tonic diaphragm electrical activity changes. Further studies are warranted to determine the impact of these findings on clinical outcomes. TRIAL REGISTRATION NUMBER: NCT02480205.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".