Cardiopulmonary resuscitation coaching for resuscitation teams: A systematic review
Bibliographic record
Abstract
Aim: Cardiopulmonary resuscitation (CPR) quality is often substandard to guidelines for resuscitation teams. We aimed to investigate if the use of a CPR coach as part of the resuscitation team can improve teamwork, quality of care, and patient outcomes during simulated and clinical cardiac arrest resuscitation. Methods: We searched PubMed, Embase, and Cochrane from inception until October 9, 2024 for randomized trials and observational studies. We assessed risk of bias using Cochrane tools and assessed the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation approach. PROSPERO CRD42024603212. Results: We screened 505 records and included 7 studies. Overall, 6 were randomized studies involving pediatric resuscitation of which 4 studies were secondary analyses of one simulation-based trial, and one was an observational study on adult out-of-hospital cardiac arrest. Reported outcomes were: CPR performance in a simulated setting (n = 3), workload in a simulated setting (n = 2), adherence to guidelines in a simulated setting (n = 1), team communication in a simulated setting (n = 1), and clinical CPR performance (n = 1). All studies suggested improved CPR quality and guideline adherence when using a CPR coach compared to not using a coach. Risk of bias varied from low to critical and the certainty of evidence across outcomes was low or very low. Conclusions: We identified low- to very-low certainty of evidence supporting the use of a CPR coach as part of the resuscitation team in order to improve CPR quality and guideline adherence. However, further research is needed, in particular for clinical performance and 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 | 0.018 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".