Abstract 247: Development and Evaluation of Standardized Communication for In-Hospital Resuscitation
Bibliographic record
Abstract
Background: Recommendations for “standardized” communication during cardiopulmonary resuscitation (CPR) events to reduce interruptions in chest compressions (CCs) are lacking. We aimed to achieve consensus on standardized communication during in-hospital CPR, evaluate feasibility and potential to decrease CC pauses, mental task load, and frustration. Methods: Modified Delphi consensus surveys of International Liaison Committee on Resuscitation taskforce members to identify preferred communication for in-hospital cardiac arrest teams. Consensus was reached after-survey teleconference resolution by a team of interdisciplinary experts in communication and CPR. Feasibility was tested in 8 simulated CPR scenarios with physician team leaders randomized (1:1) to ‘standardized’ communication vs. “usual best practice”, closed loop communication. Team leaders watched a 3-min video and received a 10-min training on the assigned intervention before the CPR scenario. NASA task load index questionnaires were used to assess mental task load and frustration (0=lowest to 100=highest). Results: Modified Delphi consensus on standardized communication included: 1) preparing the team 15-30 seconds before interrupting CCs, 2) countdown for interruption synchronized with 5 CCs, 3) action words for stopping and resuming CCs, 4) action words for intubation and defibrillation. Among 8 CPR teams (4 standardized, 4 usual communication), median (Q1; Q3) CC interruption duration for rhythm check was significantly shorter, 5.3 (4.8; 6.8) vs. 9.4 (6.4; 13.0) sec for standardized vs. closed loop communication [p=0.02]. Median (Q1; Q3) scores of mental task load were 63 (33; 70) vs 85 (70; 96), [p=0.10] and frustration 10 (5; 34) vs 40 (26; 50) [p=0.11] for standardized vs. closed loop communication. Trained team leaders adhered to standardized phrases 77% of the time. Conclusion: Expert consensus for standardized communication during in-hospital CPR was achieved. Pilot simulation implementation of standardized communication demonstrated feasibility of team leader adherence, significantly decreased duration of CC interruption during rhythm checks, and a tendency to reduce team leader task load and frustration.
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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 | 0.039 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".