A Heuristic Evaluation of Three Automated External Defibrillators
Bibliographic record
Abstract
Sudden cardiac arrest (SCA) is a major cause of death in Canada and the leading cause of death in the United States and Europe with survival rates as low as 5%. Defibrillation is the key intervention for improving survival from SCA and can increase survival up to 75%. This has sparked the movement to increase public access to defibrillation by placing automated external defibrillators (AEDs) in public areas such as universities, shopping centers, and athletic centers. The effectiveness of AEDs to revive those with SCA is dependent on the design of these devices. The purpose of this study was (1) to evaluate the design features that aid or hinder the effective and timely use of three commercially available AEDs using a heuristic evaluation; (2) to rank the three AEDs in terms of overall usability based on user comments and preference, and the severity of usability heuristic violations; and (3) develop design recommendations to guide the future design of AEDs. Results of the heuristic evaluation indicated that a number of design features influence the overall usability of AEDs, such as the orientation of electrode pads on the adhesive liner. In terms of overall usability, the Philips HeartStart OnSite AED ranked the highest, followed by the Zoll AED Plus, and the HeartSine Samaritan PAD. Recommendations for future AED design include: AEDs should have redundancy in their activation mechanism; pictorials should be minimalistic in design, numbers and location; storage of the electrode pads in a clearly labeled, easily accessible and prominent location; have clearly identifiable liners with labeling indicating the need for removal from the pads; and match the orientation of the electrode pads on the adhesive liner to the correct pad orientation on the patient's chest.
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".