Optimization of manual ventilation quality using respiratory function monitoring in neonates: A two-phase intervention trial
Bibliographic record
Abstract
Objective The aim of this study is the evaluation of the impact of a respiratory function monitor (RFM, Neo100, Monivent AB, Gothenburg, Sweden) on the quality of ventilation in neonates. Methods This single-center two-phase intervention study was conducted at the Neonatal Intensive Care Unit and the delivery room of the Medical University of Vienna. Patients with clinical need for positive pressure ventilation were included in either of two consecutive study phases: (i) patients were ventilated with a hidden RFM (control) or (ii) visible RFM (intervention) during manual positive pressure ventilations. The duration of each phase was approximately six months. The primary outcome was the percentage of ventilations within a tidal volume range of 4–8 ml/kg (pV Te ). Results A total of 90 patients (GA 22–66 weeks) were included. The primary outcome was significantly higher in the intervention group with a visible RFM (53.7%, SD 22.6) than in the control group without the monitor (37.3%, SD 20.5); (p < 0.001, mean difference [i.e., change in percentage points]: 16,95% CI: 7.4–35). In terms of secondary outcomes, excessive tidal volumes (>8ml/kg), previously associated with an increased risk of brain injury, could be significantly reduced when a RFM was visible during ventilation (10.9% [IQR 26.4] vs. 29.5% [IQR 38.1]; p = 0.004). Furthermore, mask leakage could be significantly decreased (37.3% [SD 22.7] vs. 52.7% [SD 23.0]; p = 0.002). Conclusion Our results suggest that the clinical application of a RFM for manual ventilation of preterm and term infants leads to a significant improvement in ventilation parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".