Abstract 182: Risk of Cardiac Arrest by Location Type to Guide Future AED Placement in a Large Urban Center
Bibliographic record
Abstract
Introduction: Public access defibrillation (PAD) programs can improve survival from out-of-hospital cardiac arrest (OHCA). We aimed to identify high risk public locations which should be prioritized for automated external defibrillator (AED) placement. Objectives: 1) To determine the average annual incidence of cardiac arrest by location type within the study area, 2) To describe current registered PAD deployment by location type. Methods: An observational cohort study using the Resuscitation Outcomes Consortium Epistry database. We included all public location, non-traumatic, EMS-treated OHCAs from January 1, 2006 to June 30, 2010 in the City of Toronto. The total site counts in each location category were derived from a city planning database and used as the denominator in calculating average annual per site cardiac arrest incidence. AED locations were obtained from an EMS registry. Two investigators independently categorized each OHCA and AED location. Disagreements were resolved by consensus. Results: There were 1297 OHCAs, of which 571 occurred on streets and could not be associated with a specific location type, leaving 726 eligible cases. The average age was 58±17.5 years, 82.7% were male and 13.9% survived to hospital discharge. Of all included patients, 44.9% had bystander CPR attempted and 9.5% had a bystander apply a defibrillator. Table 1 contains data from the top 15 location types ranked on average annual OHCAs per individual site. There were 1616 AED covered locations in the City of Toronto. 57.3% of the registered AED sites were elementary and secondary schools, 16.0% were post secondary schools, 7.8% were offices and 4% were community or recreation centres. Conclusions: We have identified location types with highest risk for cardiac arrest. Most registered AEDs have been placed at Elementary and Secondary Schools which are relatively low risk for OHCA. Our data can guide future AED deployment to increase availability for use in public location OHCA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".