Abstract Sun1206: Emergency Medical Services Medical Director Perspectives on Intra-arrest Transport of Out-of-Hospital Cardiac Arrest: A Thematic Analysis of Expert Opinion
Bibliographic record
Abstract
Background: There is considerable heterogeneity in Emergency Medical Services (EMS) agency-level use of intra-arrest transport (IAT), the act of transporting off-scene with ongoing chest compressions. International guidelines recommend use of IAT only if in-hospital therapies are being considered (e.g. extracorporeal cardiopulmonary resuscitation (ECPR)). Objective: We sought to explore views on the value of IAT among North Carolina (NC) EMS medical directors, with comparison of urban versus rural agencies and low versus high IAT users. Methods: NC EMS medical directors were invited for semi-structured interviews, with n=13 completed as of June 2025. Co-coding of an interim sample was performed by two researchers using a combination of inductive and deductive coding via NVivo software. Interrater reliability (IRR) was calculated via Cohen’s Kappa. Using a thematic approach, we characterized views on IAT practices, with comparison across agency rural versus urban designation and low versus high IAT use. Proportions of EMS-treated medical OHCA receiving IAT in the last year were reported by each medical director. Low and high IAT users were defined as <15% and 15% or greater respectively. Results: Based on an analysis of an interim sample of 6 interviews, 8 NC agencies were represented (1 medical director oversaw three agencies): 4 urban and 4 rural. All provide advanced life support-level care. Pooled IRR between coders was 0.79. Five medical directors (2 rural, 2 urban, 1 both) were low IAT users and one (urban) was a high IAT user. Major themes included: variation in the “ideal” role of EMS in resuscitation and differences in the perceived risk of IAT affecting resuscitation quality. Rural directors generally cited long transport times to tertiary centers as a barrier to IAT while urban directors saw short transport times as a potential facilitator. Low IAT users generally viewed the role of EMS as delivering on-scene resuscitation (reserving IAT for interventions unavailable in the field (e.g. ECPR, resuscitative hysterotomy)), while the high IAT user saw the role of EMS as delivering most OHCA to resuscitation centers in a “race against the clock.” Conclusion(s): Our analysis found that heterogeneity in agency-level IAT practices in North Carolina may be due to differing risk-benefit assessments by medical directors. Further development of the IAT evidence base is needed alongside attention to dissemination and implementation.
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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 | 0.040 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".