Abstract 81: The Impact of Pre-Arrival Dispatch-Assisted CPR on Bystander CPR Rates, Time to Starting CPR and Survival From Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: Bystander CPR (BCPR) strongly influences survival from OHCA yet is provided in a minority of cases. The AHA has promulgated guidelines on provision of pre-arrival Hands-Only dispatcher-assisted telephone CPR (TCPR) instructions to increase the proportion of arrests receiving early BCPR; however, the impact of those guidelines is unknown. Objective: Evaluate the impact of implementing the TCPR guidelines on: recognition of OHCA by 911 dispatchers, time from call receipt to start of TCPR instructions, time to first chest compression (CC), prehospital ROSC, survival, and favorable neuro outcome (FNO). Methods: Dispatch audio records of OHCA in 3 large dispatch centers in Arizona (10/10-3/13) were reviewed using a standardized time-stamp methodology. Data were entered into a structured TCPR database linked to EMS and hospital outcome data. Intervention: Implementation of a 3.5-hour staff training and a guideline-based change in dispatch protocol. Results: There were 860 pre-implementation (P1) and 799 post-implementation (P2) cases. A total of 1265 cases met inclusion criteria. Outcome data collection and linkage is ongoing: ROSC, survival and neuro outcomes were available in 26%, 24% and 22% of cases, respectively. The proportion of cases receiving TCPR increased: P1 (28.7%); P2 (49.9%, p<0.001). Median time to beginning TCPR instructions decreased significantly: P1 (153 sec); P2 (129 sec, p<0.001) as did median time to first CC: P1 (198 sec); P2 (162 sec, p<0.001). Outcomes - see Table. Conclusion: The implementation of the AHA pre-arrival TCPR guidelines was associated with a significant improvement in the time-to-CPR instructions and time-to-first CCs and rate of provision of telephone-assisted bystander CPR. This small, preliminary analysis of prehospital ROSC, survival, and FNO revealed higher, but statistically non-significant, rates after the interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".