Abstract 12075: Telecommunicator CPR Intervention Improves Recognition of Cardiac Arrest and Time to First Chest Compression
Bibliographic record
Abstract
Background: Bystander cardiopulmonary resuscitation (BCPR) significantly improves survival from out-of-hospital cardiac arrest (OHCA). Telecommunicator CPR (TCPR) has been shown to increase BCPR rates, yet TCPR performance varies widely. We assess whether a bundled TCPR protocol, training, and continuous quality improvement (CQI) intervention improves TCPR performance. Methods: The intervention was implemented at a regional 9-1-1 dispatch center and had three main components: (1) the installation of the AHA-recommended 2-question TCPR protocol, (2) training to enhance telecommunicator OHCA recognition and (3) CQI with system and case-level feedback. Training included a two-hour lecture, 30 minutes of video training, and one hour of simulation training. Audio recordings of suspected OHCAs from pre (P1, 10/5/10 - 11/6/11) and post-intervention (P2, 11/7/11 - 11/4/13) periods were collected by searching center archives for calls originally processed as cardiac arrests. Recordings were analyzed according to six key TCPR metrics. Times to telecommunicator recognition of cardiac arrest, start of TCPR instructions, and initiation of chest compressions were measured using a standardized timestamp format. Results: Rates of OHCA recognition (80% to 95%, p<0.0001), TCPR instructions started (32% to 72%, p<0.0001), and bystander chest compressions initiated (18% to 70%, p<0.001) all increased significantly. Reductions in the time from call receipt to the start of TCPR instructions (176s to 133s, p<0.0001) and to the start of compressions (265s to 162s, p<0.0001) were also observed. Conclusion: This bundled protocol, training and CQI intervention was associated with a significant increase in TCPR rates and a reduction in the time to first chest compression.
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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 | 0.002 |
| 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) | 0.019 | 0.001 |
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".