Abstract 306: Use of Real-Time Audiovisual CPR Feedback is Associated With Improved CPR Quality During Patient Transfer From the Scene to the Ambulance in Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: In the simulation setting, chest compression (CC) quality is reduced when out-of-hospital cardiac arrest “patients” are prepared for transport (i.e. packaged and moved to ambulance). It is unknown whether use of real-time audiovisual CPR feedback (RTAVF) affects manual CC quality when the patient is prepared for transport during actual out-of-hospital resuscitations. We tested the hypothesis that CC quality during patient transfer would be better when using RTAVF. Methods: CC quality was monitored at two EMS agencies using an E Series monitor/defibrillator with CC sensing capability (ZOLL Medical) during the treatment of consecutive out-of-hospital cardiac arrest patients who received CC on scene and were transported with ongoing CC. Data were collected during study phases; P1: RTAVF disabled, P2: RTAVF enabled following scenario-based training. Student’s t-test and Wilcoxon-Mann Whitney tests were used to compare CC quality in P1 vs. P2 during early scene and late scene treatment. Late scene was defined as the 3 minutes prior to transport (when packaging and transfer to the ambulance occurs) and early scene was all prior minutes at the scene. Results: 211 cardiac arrest cases were included: mean age 64±15 yrs, 67% male, 8% survival to hospital discharge. The Table shows that CC quality was superior during both early scene and late scene treatment in P2, when RTAVF was enabled, vs. P1, when RTAVF was disabled. Conclusion: CC quality, particularly the CC fraction, declines when a patient is packaged for transport and moved to the ambulance. While this decline is not prevented by RTAVF, CC quality during the late scene period is significantly better when using RTAVF. Further study is required to determine the impact of this improvement on outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".