American Heart Association Automated External Defibrillator Symposium: Summary and Recommendations
Bibliographic record
Abstract
The American Heart Association (AHA) introduced public access defibrillation more than 30 years ago. Since then, we have seen the growth of public access defibrillation programs across many settings within communities. However, despite high expectations that the availability of automated external defibrillators (AEDs) and more integrated public access defibrillation programs would dramatically increase cardiac arrest survival, AEDs are used in the United States in only 4% of out-of-hospital cardiac arrests and survival rates have remained disappointingly low. In follow-up to a recent International Liaison Committee on Resuscitation report, an AED Symposium was organized by members of the AHA Emergency Cardiovascular Care Committee to establish a strategic roadmap for AED technology, education and training, and real-world use of these devices, including integration with public access defibrillation programs to meet the AHA's goal of doubling out-of-hospital cardiac arrests survival by 2030. The meeting brought together a diverse group of subject matter experts including representatives from the US Food and Drug Administration, the defibrillator industry, clinicians, and scientists. This paper summarizes the proceedings of the AED symposium and suggests a set of strategic recommendations to ultimately improve survival from cardiac arrest.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.019 |
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".