Automated external defibrillator accessibility is crucial for bystander defibrillation and survival: A registry-based study
Bibliographic record
Abstract
AIMS: Optimization of automated external defibrillator (AED) placement and accessibility are warranted. We examined the associations between AED accessibility, at the time of an out-of-hospital cardiac arrest (OHCA), bystander defibrillation, and 30-day survival, as well as AED coverage according to AED locations. METHODS: In this registry-based study we identified all OHCAs registered by mobile emergency care units in Copenhagen, Denmark (2008-2016). Information regarding registered AEDs (2007-2016) was retrieved from the nationwide Danish AED Network. We calculated AED coverage (AEDs located ≤200 m route distance from an OHCA) and, according to AED accessibility, the likelihoods of bystander defibrillation and 30-day survival. RESULTS: Of 2500 OHCAs, 22.6% (n = 566) were covered by a registered AED. At the time of OHCA, <50% of these AEDs were accessible (n = 276). OHCAs covered by an accessible AED were nearly three times more likely to receive bystander defibrillation (accessible: 13.8% vs. inaccessible: 4.8%, p < 0.001) and twice as likely to achieve 30-day survival (accessible: 28.8% vs. inaccessible: 16.4%, p < 0.001). Among bystander-witnessed OHCAs with shockable heart rhythms (accessible vs. inaccessible AEDs), bystander defibrillation rates were 39.8% vs. 20.3% (p = 0.01) and 30-day survival rates were 72.7% vs. 44.1% (p < 0.001). Most OHCAs were covered by AEDs at offices (18.6%), schools (13.3%), and sports facilities (12.9%), each with a coverage loss >50%, due to limited AED accessibility. CONCLUSIONS: The chance of a bystander defibrillation was tripled, and 30-day survival nearly doubled, when the nearest AED was accessible, compared to inaccessible, at the time of OHCA, underscoring the importance of unhindered AED accessibility.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".