The impact of locked cabinets for automated external defibrillators (AEDs) on cardiac arrest and AED outcomes: A scoping review
Bibliographic record
Abstract
Background: Rapid public defibrillation with automated external defibrillators (AEDs) is critical to improving out-of-hospital cardiac arrest survival. Concerns about AED theft and vandalism have led to implementing security measures, including locked cabinets. This scoping review, conducted as part of the evidence review for the International Liaison Committee on Resuscitation, explores the impact of securing AEDs in locked cabinets. Methods: Searches of Medline, Embase, Cochrane, CINAHL (from database inception to 25/5/2024) and Google Scholar (first 200 articles). Studies of any type or design, published with an English abstract, examining the impact of locked AED cabinets were included. The included studies were grouped by outcomes, and an iterative narrative synthesis was performed. Results: We screened 2,096 titles and found 10 relevant studies: 8 observational studies (4 published as conference abstracts) and 2 simulation studies. No study reported patient outcomes. Studies reported data on between 36 and 31,938 AEDs. Most studies reported low rates (<2%) of theft/missing/vandalism, including AEDs that were accessible 24/7. The only study comparing unlocked and locked cabinets showed minimal difference in theft and vandalism rates (0.3% vs. 0.1%). Two simulation studies showed significantly slower AED retrieval when additional security measures, included locked cabinets, were used. A survey of first responders reported half (25/50) were injured while accessing an AED that required breaking glass to access. Conclusion: The limited literature suggests that vandalism and the loss of AEDs are rare and occur in locked and unlocked cabinets. Research on this topic is needed that focuses on real-life retrieval 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 | 0.017 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".