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Record W4389976024 · doi:10.1161/circ.148.suppl_1.247

Abstract 247: Assessing Feasibility of Proposed Extracorporeal Cardiopulmonary Resuscitation Programs in Scotland via Geospatial Modelling

2023· article· en· W4389976024 on OpenAlexaff
Kwan Leung, Louise Hartley, Stuart Gillon, Lyle Moncur, Timothy C. Y. Chan, Steven Short, Gareth Clegg

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineExtracorporeal cardiopulmonary resuscitationCardiopulmonary resuscitationConventional PCIReturn of spontaneous circulationEmergency medicinePercutaneous coronary interventionMedical emergencyIntervention (counseling)Internal medicineIntensive care medicineResuscitationMyocardial infarctionNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.138
GPT teacher head0.339
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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