Assessing the human factors involved in chest compression with superimposed sustained inflation during neonatal and paediatric resuscitation: A randomized crossover study
Bibliographic record
Abstract
Background: A new cardiopulmonary resuscitation technique, chest compressions with sustained inflation (CC + SI) might be an alternative to both the neonatal [3:1compressions to ventilations (3:1C:V)] and paediatric [chest compression with asynchronous ventilation (CCaV)] approaches. The human factors associated with this technique are unknown. We aimed to compare the physical, cognitive, and team-based human factors for CC + SI to standard CPR (3:1C:V or CCaV). Methods: Randomized crossover simulation study including 40 participants on 20 two-person teams. Workload [National Aeronautics and Space Administration Task Load Index (NASA-TLX)], crisis resource management skills (CRM) [Ottawa Global Rating Scale (OGRS)], and debrief analysis were compared. Results: There was no difference in paired NASA-TLX scores for any dimension between the CC + SI and standard, adjusting for CPR order. There was no difference in CRM scores for CC + SI compared to standard. Participants were less familiar with CC + SI although many found it simpler to perform, better for transitions/switching roles, and better for communication. Conclusions: The human factors are no more physically or cognitively demanding with CC + SI compared to standard CPR (NASA-TLX and participant debrief) and team performance was no different with CC + SI compared to standard CPR (OGRS score).
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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) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".