Data-informed debriefing for cardiopulmonary arrest: A randomized controlled trial
Bibliographic record
Abstract
To determine if data-informed debriefing, compared to a traditional debriefing, improves the process of care provided by healthcare teams during a simulated pediatric cardiac arrest. We conducted a prospective, randomized trial. Participants were randomized to a traditional debriefing or a data-informed debriefing supported by a debriefing tool. Participant teams managed a 10-minute cardiac arrest simulation case, followed by a debriefing (i.e. traditional or data-informed), and then a second cardiac arrest case. The primary outcome was the percentage of overall excellent CPR. The secondary outcomes were compliance with AHA guidelines for depth and rate, chest compression (CC) fraction, peri-shock pause duration, and time to critical interventions. A total of 21 teams (84 participants) were enrolled, with data from 20 teams (80 participants) analyzed. The data-informed debriefing group was significantly better in percentage of overall excellent CPR (control vs intervention: 53.8% vs 78.7%; MD 24.9%, 95%CI: 5.4 to 44.4%, p = 0.02), guideline-compliant depth (control vs. intervention: 60.4% vs 85.8%, MD 25.4%, 95%CI: 5.5 to 45.3%, p = 0.02), CC fraction (control vs intervention: 88.6% vs 92.6, MD 4.0%, 95%CI: 0.5 to 7.4%, p = 0.03), and peri-shock pause duration (control vs intervention: 5.8 s vs 3.7 s, MD −2.1 s, 95%CI: −3.5 to −0.8 s, p = 0.004) compared to the control group. There was no significant difference in time to critical interventions between groups. When compared with traditional debriefing, data-informed debriefing improves CPR quality and reduces pauses in CPR during simulated cardiac arrest, with no improvement in time to critical interventions.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".