Cost Effectiveness of Defibrillation by Targeted Responders in Public Settings
Bibliographic record
Abstract
BACKGROUND: Out-of-hospital cardiac arrest is frequent and has poor outcomes. Defibrillation by trained targeted nontraditional responders improves survival versus historical controls, but it is unclear whether such defibrillation is a good value for the money. Therefore, this study estimated the incremental cost effectiveness of defibrillation by targeted nontraditional responders in public settings by using decision analysis. METHODS AND RESULTS: A Markov model evaluated the potential cost effectiveness of standard emergency medical services (EMS) versus targeted nontraditional responders. Standard EMS included first-responder defibrillation followed by advanced life support. Targeted nontraditional responders included standard EMS supplemented by defibrillation by trained lay responders. The analysis adopted a US societal perspective. Input data were derived from published or publicly available data. Future costs and effects were discounted at 3%. Monte Carlo simulation and sensitivity analyses assessed the robustness of results. Standard EMS had a median of 0.47 (interquartile range [IQR]=0.32 to 0.69) quality-adjusted life years and a median of 14 100 dollars (IQR=8600 dollars to 21 900 dollars) costs per arrest. Targeted nontraditional responders in casinos had an incremental cost of a median 56 700 dollars (IQR=44 100 dollars to 77 200 dollars) per additional quality-adjusted life year. The results were sensitive to changes in time to defibrillation, incidence of arrest, and number of devices required to implement rapid defibrillation. CONCLUSIONS: Where cardiac arrest is frequent and response time intervals are short, rapid defibrillation by targeted nontraditional responders may be a good value for the money compared with standard EMS. The incidence of arrest should be considered when choosing locations to implement public access defibrillation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".