Abstract 247: Assessing Feasibility of Proposed Extracorporeal Cardiopulmonary Resuscitation Programs in Scotland via Geospatial Modelling
Bibliographic record
Abstract
Introduction: Extracorporeal cardiopulmonary resuscitation (ECPR) may improve outcomes for out-of-hospital cardiac arrest (OHCA) where return of spontaneous circulation (ROSC) is not achieved. We estimated the number of patients who may benefit from proposed in-hospital or pre-hospital ECPR programs in Scotland. Methods: EMS-treated atraumatic OHCAs occurring in Scotland between Apr. 2017-Mar. 2022 were included. We identified those likely to benefit (age 16-70, initial rhythm VF/pVT, no ROSC) and those possibly likely to benefit (age 16-70, initial rhythm PEA, no ROSC) from ECPR. To achieve timely ECPR initiation within 60 mins, we computed the number of eligible OHCAs within 15 mins’ drive time surrounding each of the 3 ECPR-capable hospitals, 6 percutaneous coronary intervention (PCI)-capable hospitals, and 28 adult emergency departments (ED) for in-hospital ECPR programs. We then computed the number of eligible OHCAs within a 45-minute drive time surrounding each of the 28 EDs and 140 ambulance stations and determined locations allowing one dedicated pre-hospital ECPR service to reach the greatest number of eligible OHCAs. We accounted for service availability based on 8 and 16-hour availability on weekdays and 24/7 availability. Results: Of the 8,962 OHCAs included, 1,108 (12.3%) were classified likely to benefit, 4,675 (52.2%) possibly likely to benefit, and 3,179 (35.5%) unlikely to benefit from ECPR. In-hospital ECPR scenarios covered up to 12.5% of eligible OHCAs at ECPR-capable hospitals, 20.9% at PCI-capable hospitals, and 64.6% at all EDs. Pre-hospital ECPR scenarios with one crew covered up to 51.0% and 54.1% when based at an ED or ambulance station respectively, which were higher than respective in-hospital scenarios at ECPR-capable or PCI-capable hospitals, and were generally comparable to ECPR availability at all 28 EDs. Conclusion: A pre-hospital ECPR service can generally reach greater numbers of OHCAs compared to in-hospital ECPR programs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".