Change in Cardiopulmonary Resuscitation Performance Over Time During Simulated Pediatric Cardiac Arrest and the Effect of Just-in-Time Training and Feedback
Bibliographic record
Abstract
OBJECTIVES: Effective cardiopulmonary resuscitation (CPR) is critical to ensure optimal outcomes from cardiac arrest, yet trained health care providers consistently struggle to provide guideline-compliant CPR. Rescuer fatigue can impact chest compression (CC) quality during a cardiac arrest event, although it is unknown if visual feedback or just-in-time training influences change of CC quality over time. In this study, we attempt to describe the changes in CC quality over a 12-minute simulated resuscitation and examine the influence of just-in-time training and visual feedback on CC quality over time. METHODS: We conducted secondary analysis of data collected from the CPRCARES study, a multicenter randomized trial in which CPR-certified health care providers from 10 different pediatric tertiary care centers were randomized to receive visual feedback, just-in-time CPR training, or no intervention. They participated in a simulated cardiac arrest scenario with 2 team members providing CCs. We compared the quality of CCs delivered (depth and rate) at the beginning (0-4 minutes), middle (4-8 minutes), and end (8-12 minutes) of the resuscitation. RESULTS: There was no significant change in depth over the 3 time intervals in any of the arms. There was a significant increase in rate (128 to 133 CC/min) in the no intervention arm over the scenario duration (P < 0.05). CONCLUSIONS: There was no significant drop in CC depth over a 12-minute cardiac arrest scenario with 2 team members providing compressions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".