Abstract 232: Predicted One Third Improvement in Survival for Shockable Rhythms of a Proposed First Responder and Drone Automated External Defibrillator Delivery Program in North Carolina
Bibliographic record
Abstract
Background: Drone-delivered AEDs can improve access to defibrillation for OHCA. If drone-delivered AEDs arrive at the OHCA before EMS, then the OHCA’s first recorded rhythm may differ from that recorded by EMS. We estimate the clinical impact of a proposed program of first responder (FR) and drone AED delivery in North Carolina while accounting for this potential unobserved change in shockable rhythm. Methods: Using the CARES registry data, we identified OHCAs in 48 counties between Jan. 2013-Dec. 2019. OHCAs were stratified by whether they were bystander-witnessed. For each subpopulation three separate logistic regression models were trained to predict: 1) shockable initial rhythm 2) survival to discharge for shockable OHCAs and 3) survival to discharge for non-shockable OHCAs. Predictions from these models were combined to predict overall survival of a proposed program of first responder and drone AED delivery optimized for 5-minute coverage (proportion of OHCAs with response within 5 minutes). Results: We included 21,987 OHCAs to fit the outcome prediction models and used these models to predict the estimated clinical impact on the OHCAs with improved response time due to the FR and drone intervention (n=16, 321). For witnessed OHCAs, predicted rates of shockable initial rhythm increased by 14.9% (29.5% to 33.9%), and predicted rates of survival to discharge rose by 31.1% (13.2% to 17.3%). For unwitnessed OHCAs, predicted rates of shockable initial rhythm increased by 5.1% (11.8% to 12.4%), and predicted survival to discharge rose by 14.6% (4.8% to 5.5%). Witnessed OHCAs with <5-min response had the largest predicted benefit with survival rates increasing by 34.5% (14.5% to 19.5%). Conclusion: When accounting for changes in the likelihood of shockable rhythm due to improved response time, we estimated that a FR and drone AED delivery program may substantially improve survival rates of witnessed arrests and marginally improve survival rates of unwitnessed arrests.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".