A Descriptive Analysis of Emergency Medical Service Systems Participating in the Resuscitation Outcomes Consortium (ROC) Network
Bibliographic record
Abstract
BACKGROUND: The optimal Emergency Medical Services (EMS) system characteristics have not been defined, resulting in substantial variability across systems. The Resuscitation Outcomes Consortium (ROC) is a United States-Canada research network that organized EMS agencies from 11 different systems to perform controlled trials in cardiac arrest and life-threatening trauma resuscitation. OBJECTIVES: To describe EMS systems participating in ROC using a novel framework. METHODS: Standardized surveys were created by ROC investigators and distributed to each site for completion. These included separate questions for individual hospitals, EMS agencies, and dispatch centers. Results were collated and analyzed by using descriptive statistics. RESULTS: A total of 264 EMS agencies, 287 hospitals, and 154 dispatch centers were included. Agencies were described with respect to the type (fire-based, non-fire governmental, private), transport status (transport/non-transport), and training level (BLS/ALS). Hospitals were described with regard to their trauma designation and the presence of electrophysiology and cardiac catheterization laboratories. Dispatch center characteristics, including primary versus secondary public safety answering point (PSAP) status and the use of prearrival instructions, were also described. Differences in EMS system characteristics between ROC sites were observed with multiple intriguing patterns. Rural areas and fire-based agencies had more EMS units and providers per capita. This may reflect longer response and transport distances in rural areas and the additional duties of most fire-based providers. In addition, hospitals in the United States typically had catheterization laboratories, whereas Canadian hospitals generally did not. The vast majority of both primary and secondary PSAPs use computer-aided dispatch. CONCLUSIONS: Similarities and differences among EMS systems participating in the ROC network were described. The framework used in this analysis may serve as a template for future EMS research.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".