Mask ventilation using volume-targeted neonatal ventilator for neonatal resuscitation: a randomised cross-over simulation study
Bibliographic record
Abstract
OBJECTIVE: To use simulations to compare a novel mask ventilation method using a neonatal ventilator, with mask ventilation using a T-piece resuscitator, to study human factors prior to clinical testing. DESIGN: Prospective randomised cross-over simulation study. Participants were briefly trained to use a neonatal ventilator for mask ventilation. Each participant was fitted with eye-tracking glasses to record visual attention (VA) and performed two simulated preterm neonatal resuscitations in a randomised sequence. SETTING: In situ in a neonatal resuscitation room within a Level 3 neonatal intensive care unit. PARTICIPANTS: Healthcare professionals (HCPs) trained in neonatal resuscitation with experience as team leaders. INTERVENTIONS: Semiautomated, ventilator-based, volume-targeted positive pressure mask ventilation (VTV-PPV) versus manual mask ventilation via T-piece device (T-piece PPV). MAIN OUTCOME MEASURES: Subjective workload (Surgical Task Load Index, SURG-TLX), VA, quantitative and qualitative postsimulation survey responses. RESULTS: Thirty HCPs participated. HCPs reported higher total SURG-TLX scores (43.5/120 vs 33.8/120) and higher scores in mental demand (8.2/20 vs 5.6/20), physical demand (6.6/20 vs 5.1/20), task complexity (8.2/20 vs 6/20) and situational stress (8.3/20 vs 5.9/20) for VTV-PPV. Temporal demand and distraction scores were similar. While participants took longer to complete VTV-PPV simulations, participants dedicated similar a %VA to the mannikin and T-piece gauges or ventilator screen. More participants increased the rate of ventilation during VTV-PPV; other corrective steps were similar. Overall, participants rated VTV-PPV positively. Participants identified potential challenges with physical ergonomics, cognition and teamwork. CONCLUSION: Using a neonatal ventilator to perform volume-targeted PPV is feasible, but human factors need to be considered.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".