Development and Evaluation of a Novel Resuscitation Teamwork Model for Out-of-Hospital Cardiac Arrest in the Emergency Department
Bibliographic record
Abstract
Study objective Cardiopulmonary resuscitation (CPR) is critical for out-of-hospital cardiac arrest patients but is prone to rapid changes and errors. Effective teamwork and leadership are essential for high-quality CPR. We aimed to introduce the Airway-Circulation-Leadership-Support (A-C-L-S) teamwork model in the emergency department (ED) to address these challenges. Methods The study comprised 2 phases. The development phase involved reviewing CPR videos, categorizing problems, and formulating strategies using the Systems Engineering Initiative for Patient Safety model. Resuscitation tasks were organized into A-C-L-S domains using hierarchical task analysis. Equipment and environmental deficits were optimized ergonomically with a pit-crew style arrangement. Mnemonics enhanced teamwork and leadership. The evaluation phase assessed postimplementation ED resuscitation team performance, focusing on adherence, timeliness, and quality of A-C-L-S tasks. Results The development phase produced a structured teamwork model, assigning tasks, tools, mnemonics, and positions based on A-C-L-S domains. The A-team manages the airway and optimizes end-tidal CO 2 levels; the C-team focuses on high-quality chest compressions and defibrillation. Leadership coordinates resuscitation efforts using goal-directed mnemonics (DABCD 2 E 3 ), whereas the S-team handles medications, timekeeping, and recording. The evaluation phase showed improvements in adherence and timeliness of A-C-L-S tasks, with sustained increases in chest compression fraction before mechanical CPR, from 67.2% preimplementation to 83.0% postimplementation, 89.1% after 1 year, and 86.1% after 2 years. Overall, chest compression fraction also improved from 81.7% to 88.6%, peaking at 92.2% after 1 year and maintaining 90.8% after 2 years. Conclusion The A-C-L-S teamwork model is feasible, applicable, and effective. Further research is needed to assess its influence on patient outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".