Neurally-adjusted ventilatory assist (NAVA) in children: a systematic review.
Bibliographic record
Abstract
INTRODUCTION: Application of mechanical ventilation in spontaneously breathing children remains a challenge for several reasons: mainly, small tidal volumes and high respiratory rates, especially in the presence of leaks, interfere with patient-ventilator synchrony. Leaks also cause unreliable monitoring of respiratory drive and respiratory rate. Furthermore, ventilator adjustment must take into account that infants have strong vagal reflexes, demonstrate central apnea and periodic breathing, with a high variability in breathing pattern. Neurally-adjusted ventilatory assist (NAVA) is a mode of ventilation whereby the timing and amount of ventilatory assist is controlled by the patient's neural respiratory drive. Since NAVA uses the diaphragm electrical activity (Edi) as the controller signal, it is possible to deliver synchronized assist, both invasively and non-invasively (NIV-NAVA), to follow the variability in breathing pattern, and to monitor patient respiratory drive, independent of leaks. EVIDENCE ACQUISITION: This article provides a review of the scientific literature pertaining to the use of NAVA in children (neonatal and pediatric age groups). Both the invasive and non-invasive NAVA publications are summarized, as well as the use of Edi monitoring. EVIDENCE SYNTHESIS: Overall, the use of NAVA and Edi monitoring is feasible and safe. Compared to conventional ventilation, NAVA improves patient-ventilator interaction, and provides lower peak inspiratory pressure. CONCLUSIONS: Evidence from a few trials suggests improved comfort, less sedation, and reduced length of stay.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".