Abstract 206: Cardiopulmonary Resuscitation Process Measures Associated With Return of Spontaneous Circulation in Non-Shockable Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Purpose: Survival rates after non-shockable out-of-hospital cardiac arrest (OHCA) remain low despite advances in resuscitation. Cardiopulmonary resuscitation (CPR) process measures may inform treatment strategies. We hypothesized that CPR process measures would be associated with return of spontaneous circulation (ROSC) and patient electrocardiogram (ECG) transitions. Methods: We obtained defibrillator monitor data for emergency medical service (EMS)-treated non-shockable OHCA from the Resuscitation Outcomes Consortium (ROC), an OHCA research network (U.S./Canada). We extracted ECG data from EMS defibrillator files and parsed cases into compression-free analyzable segments using custom MATLAB software. Two data abstractors classified segment rhythms as PEA, asystole, ventricular fibrillation (VF), pulseless ventricular tachycardia (PVT), or ROSC. We calculated CPR process measures (average rate, depth, duration, leaning proportion, chest compression fraction, and duty cycle) for CPR bouts preceding every ECG segment. We used mixed effects models controlling for subject to test associations between individual CPR process measures and the bout-level outcomes ROSC and shockable rhythm. Results: We analyzed 1893 cases consisting of 7981 CPR bouts. Case initial rhythms were asystole (68.2%), PEA (24.9%), or NSA-AED (6.9%). Segment rhythm classifications were asystole (78.1%), PEA (20.4%), ROSC (5.5%), VF (1.4%), and PVT (0.07%). Regression model results are shown in Table 1. Chest compression fraction was most strongly associated with ROSC and shockable rhythm. Depth was also associated with shockable rhythm. Leaning proportion and duty cycle were not associated with either outcome. Conclusions: In cases of non-shockable OHCA, CPR quality measures were associated with ROSC and transition to a shockable rhythm at the bout level.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".