Assessing automated external defibrillators in preventing deaths from sudden cardiac arrest: An economic evaluation
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the cost-effectiveness of on-site automated external defibrillators (AEDs) in the initial management of cardiac arrest in Ontario. METHODS: This was a cost-effectiveness analysis based on published literature and data from the Canadian Institute of Health Information. The participants were fictitious male and female cardiac arrest patients who were initially managed with on-site AEDs, compared with similar patients managed without on-site AEDs. This group included a subgroup of high-risk patients (i.e., heart failure and left ventricular ejection fraction<35 percent). The analysis was conducted in a variety of settings including hospitals and homes in Ontario, Canada. The main outcome evaluated was cost per quality-adjusted life-year (QALY) gained from a payer's perspective. RESULTS: Cost per QALY (all costs reported in Canadian dollars) was $12,768 when AEDs were deployed in hospitals, $511,766 when deployed in office buildings, $2,360,023 when deployed in apartment buildings, $87,569 when deployed in homes of high-risk patients, and $1,529,371 when deployed in homes of people older than 55 years of age. CONCLUSIONS: Indiscriminate deployment of AEDs is not a cost-effective means of improving health outcomes of cardiac arrest. Their use should be restricted to emergency response programs, high-risk sites (such as hospitals), and high-risk patients.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".