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 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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".