Derivation of a Termination-of-resuscitation Guideline for Emergency Medical Technicians Using Automated External Defibrillators
Bibliographic record
Abstract
OBJECTIVES: To determine the association between characteristics of cardiac arrest and survival to hospital discharge following failed resuscitation by defibrillation-trained emergency medical technicians (EMT-Ds), and to propose an out-of-hospital termination-of-resuscitation (TOR) guideline for EMT-Ds. METHODS: A 22-month retrospective review of 700 out-of-hospital primary cardiac arrest patients in a large emergency medical services (EMS) system who received exclusively EMT-D care. RESULTS: Seven hundred primary cardiac arrest patients were identified. Follow-up was obtained in 662 cases (94.6%). Of these, 36 (5.4%) achieved a return of spontaneous circulation (ROSC) prior to transport. Among the 626 patients who failed to achieve ROSC at any time, two (0.3%) survived to discharge. Multivariate analysis showed that ROSC at any time had the strongest association with survival [odds ratio (OR) 45.5; 95% confidence interval (95% CI) = 8.5 to 243.7]. A shock prior to transport (OR 6.9; 95% CI = 1.2 to 40.3) and cardiac arrest witnessed by EMS personnel (OR 4.4; 95% CI = 1.0 to 18.5) were also independently associated with survival. These variables were incorporated into a TOR guideline. The guideline was 100% sensitive (95% CI = 99.1 to 100) in identifying survivors and had 100% negative predictive value (95% CI = 75.3 to 100) for identifying nonsurvivors of out-of-hospital cardiac arrest in the study population. CONCLUSIONS: In this EMS system, cardiac arrest patients may be considered for out-of-hospital TOR following EMT-D resuscitation attempts when there has been no ROSC, no shock has been given, and the arrest was not witnessed by EMS personnel. These guidelines require prospective validation.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".