Abstract Sun904: Variability of Automated External Defibrillator Signage Across Six Continents
Bibliographic record
Abstract
Introduction/Background: Despite evidence supporting their efficacy in improving out-of-hospital cardiac arrest survival, public utilization of automated external defibrillators (AEDs) remains low. Characteristics of AED signage varies considerably, despite international standards developed by the International Liaison Committee on Resuscitation (ILCOR). The extent of this variability, which may lead to confusion and reduced AED use, is unknown. Research Questions/Hypothesis: We hypothesize that there is variability in AED signage globally. Goals/Aims: We aimed to describe variability of AED signage across six continents, focusing on shape, color, and the presence of heart, cross, electric bolt, and arrow symbols. Methods/Approach: We reviewed a convenience sample of publicly available AED signage found online from countries in Europe, North American, Africa, Australia, Asia, and South America. We used internet search terms such as “AED,” “AED sign,” “AED signage,” “Defibrillator sign,” “Defibrillator signage,” followed by the country name. Characteristics including shape, color, and the presence of cross, heart, electric bolt, and arrow symbols were recorded in a data collection form. We reported frequencies and proportions of these characteristics for the entire sample and stratified by continent. Fisher's exact tests were used to compare proportions across continents. Statistical significance was set at p<0.05. Results/Data: Between December 2024 and May 2025, 142 AED signage images were collected. After excluding 22 images depicting actual AEDs or carrying cases, 120 images were analyzed. Continents included Europe (46 images, 38.3%), North America (28, 23.3%), Africa (17, 14.2%), Australia (15, 12.5%), Asia (9, 7.5%), and South America (5, 4.2%). Most signs were rectangular (81.7%), had a green background (43.3%), white text (48.3%), and included symbols of a cross (43.3%), heart (98.3%), and electric bolt (91.7%). Of the signs displaying a cross (52), 86.5% were white. Of those with a heart (118), 52.5% were white. Of those with an electric bolt (110), 44.6% were green. Only 33.3% of signs included an arrow. The distribution of all signage characteristics varied significantly across the six continents (all p<0.05). Conclusions: We found variability in AED signage characteristics, with significant differences in shapes, colors, and images. Further studies may investigate whether signage characteristics are associated with improved AED recognition and use.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".