Automated External Defibrillator Geolocalization with a Mobile Application, Verbal Assistance or No Assistance: A Pilot Randomized Simulation (AED G-MAP)
Bibliographic record
Abstract
OBJECTIVE: Shockable rythms are common among victims of witnessed public out-of-hospital cardiac arrest (OHCA), but bystander defibrillation with a public automated external defibrillator (PAED) is rare. Instructions from the emergency medical dispatcher and mobile applications were developed to expedite the localization of PAEDs, but their effectiveness has not been compared. METHODS: Participants were enrolled in a three-armed randomized simulation where they witnessed a simulated OHCA on a university campus, were instructed to locate a PAED and provide defibrillation. Participants were stratified and randomized to: (1) no assistance in finding the PAED, (2) assistance from a geolocalization mobile application (AED-Quebec), or (3) verbal assistance. Data collectors tracked each participant's time elapsed and distance traveled to shock. RESULTS: Of the 52 volunteers participating in the study (46% male, mean age 37), 17 were randomized to the no assistance group, 18 to the mobile application group and 17 to the verbal group. Median (IQR) time to shock was, respectively, 10:00 min (7:49-10:00), 9:44 (6:30-10:00), and 5:23 (4:11-9:08), with statistically significant differences between the verbal group and the other groups (p ≤ 0.01). The success rate for defibrillation in <10 minutes was 35%, 56% and 76%. Multivariate regression of all participants pooled showed that knowledge of campus geography was the strongest predictor of shock in <10 minutes (aOR =14.3, 95% CI 1.85-99.9). Among participants without prior geographical knowledge, verbal assistance provided a trend towards decreased time to shock, but the differences over no assistance (7:28 vs. 10:00, p = 0.10) and over the mobile app (7:28 vs. 10:00, p = 0.11) were not statistically significant. CONCLUSION: In a simulated environment, verbally providing OHCA bystanders with the nearest PAED's location appeared to be effective in reducing the time to defibrillation in comparison to no assistance and to an AED geolocalizing mobile app, but further research is required to confirm this hypothesis, ascertain the external validity of these results, and evaluate the real-life implications of these strategies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".