Abstract 242: A Standardized Template for Measuring and Reporting Dispatch Prearrival CPR
Bibliographic record
Abstract
Introduction: Dispatch-Assisted CPR (DACPR) is a method for improving bystander response and survival from out-of-hospital cardiac arrest (OHCA). No standardized methodology exists for measuring and reporting this intervention. We developed a template for collecting, analyzing, and reporting DACPR. Methods: Five trained evaluators reviewed 161 suspected OHCA audio recordings from an urban 911 dispatch center. Key metrics in the evaluation of pre-arrival CPR instructions were recorded on a structured data form: dispatch recognition of OHCA, time interval to initiation of CPR instructions, and interval to first chest compression. Using the tool, evaluators identified common themes in the delivery of DACPR. Results: See Figure for template and results. Conclusion: Utilizing key components of the AHA DACPR Scientific Advisory Statement, we developed a method for collecting, analyzing, and reporting key metrics for DACPR that could act as a template to help improve the DACPR process and increase rates of Dispatch-Assisted bystander CPR (DA-BCPR). DACPR data collected in this manner may help identify targets for improvement in the prehospital response to OHCA. In addition, the template may support collaborative, inter-system investigations by providing a standardized, Utstein-type approach to data reporting.
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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 | 0.042 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".