Respiratory metrics of neonatal positive pressure ventilation on different ventilatory rates: A simulation study
Bibliographic record
Abstract
Background: Effective ventilation is the core of neonatal resuscitation (NR). T-piece resuscitators (TPR) and self-inflating bags (SIB) are the two most widely utilized resuscitation devices. Nevertheless, limited information is available regarding the respiratory metrics during NR with these devices. Objectives: This study aimed to evaluate the respiratory metrics at different ventilatory rates (VR) using a TPR or SIB during NR training. Methods: An observational, simulation study was conducted during a NR training course. The participants were instructed to perform positive pressure ventilation at predetermined pressures and varying rates using TPR and SIB. They were subsequently grouped into three categories based on their actual VR: 20-40 breaths per minute (bpm) (SlowVR), 40-60 bpm (StdVR), and 60-80 bpm (FastVR). Respiratory metrics were recorded and analyzed using a neonatal active lung model (NALM). Results: Of the 71 participants in the training course, data from 66 were validated by analyzing 198 ventilations. In general, the participants manipulated the TPR slightly slower than the SIB. Notably, the positive end-expiratory pressure (PEEP) detected via TPR in the NALM was substantially higher, whereas the tidal volume (Tv) and minute volume (MV) with TPR were significantly smaller than those with SIB (p < 0.05). A significant decrease in the peak alveolar pressure (palva) was observed with faster TPR ventilation (p < 0.001), whereas no such reduction was observed with SIB (p = 0.103). Additionally, faster VR correlated positively with higher PEEP levels for both TPR (F = 7.543, p = 0.002) and SIB (F = 7.720, p = 0.002) and inversely with smaller Tv for both TPR (F = 19.239, p < 0.001) and SIB (F = 14.937, p < 0.001). However, no significant differences in MV were observed across the different VR for either TPR or SIB (both p > 0.05). Conclusions: Faster VR were inversely associated with smaller Tv but increased PEEP in both devices. Despite the guidelines of NR, VR exceeding 60 bpm with TPR might sometimes be used, was associated with excessive PEEP in TPR, which may not be a safe in clinical practice. The effect of varying VR on MV was relatively minor for both TPR and SIB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".