Abstract 185: Optimization of Public Access Defibrillators Compared to Actual Deployment: An <i>In Silico</i> Trial
Bibliographic record
Abstract
Introduction: Unguided placement of automated external defibrillators (AEDs) often leads to placements in low risk areas and locations with limited temporal availability. Mathematical optimization may improve AED placements and increase AED use in out-of-hospital cardiac arrests (OHCAs). Aim: To conduct the first in silico public AED location trial to determine whether optimization models (interventions) trained on historical OHCA data will recommend AED locations that significantly improve OHCA coverage on prospective OHCAs, compared to locations of actually deployed AEDs (control). Methods: We identified all public OHCAs of presumed cardiac cause (1994-2016) and already deployed AEDs (2007-2016) in Copenhagen, Denmark. We computed the number of OHCAs that occurred within 100m of a temporally available AED after it was deployed (“OHCA coverage”). We then divided 2007-2016 into 30-day intervals and determined the number of AEDs deployed in each interval. Using previously validated optimization models, we determined an equal number of optimal AED locations in each time interval, either indoor locations with actual availability (intervention #1) or outdoor locations with 24/7 availability (intervention #2). OHCA coverage was calculated for the interventions similarly to the already deployed AEDs. Finally, we repeated the analysis 25 times to evaluate sensitivity and generate confidence intervals, by randomizing the location and time of the OHCAs. Results: A total of 2,149 public OHCAs (744 between 2007-2016) and 1,573 registered AEDs were identified. OHCA coverage of actually deployed AEDs was 22.3% (166 of 744 OHCAs). For optimally located indoor AEDs, mean OHCA coverage was 32.6% (mean: 242.5 OHCAs; 95% CI: 239.7 - 245.3). For optimally located outdoor AEDs, mean OHCA coverage was 43.9% (mean: 326.6 OHCAs; 95% CI: 324.0 - 329.2). Conclusions: Optimizing AED locations in a real-time deployment approach mimicking the time horizon of actual AED deployment in Copenhagen, Denmark results in significantly higher OHCA coverage compared to the actual AEDs deployed. Between the two interventions, optimal locations that are 24/7 available significantly outperform optimal indoor locations with more limited temporal availability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".