International Consensus on Evidence Gaps and Research Opportunities in Extracorporeal Cardiopulmonary Resuscitation for Refractory Out‐of‐Hospital Cardiac Arrest: A Report From the National Heart, Lung, and Blood Institute Workshop
Bibliographic record
Abstract
The increased accessibility of extracorporeal membrane oxygenation following the COVID-19 pandemic and the publication of the first randomized trial of extracorporeal cardiopulmonary resuscitation (ECPR) prompted the National Heart, Lung, and Blood Institute to sponsor a workshop on ECPR. Two more randomized trials have since been published in 2022 and 2023. Based on the combined findings and review of the evidence, an international panel of authors identified gaps in science, inequities in care and diversity in outcomes, and suggested research opportunities and next steps. The science pertaining to ECPR would benefit from the United States contributing uniform data to existing registries and sharing common data with the ELSO (Extracorporeal Life Support Organization) international registry to increase the sample size for observational research. In addition, well-designed efficacy trials, recruiting across different regions of care evaluating long-term follow-up, including patient reported outcomes, cost effectiveness, and equity measures, would contribute significantly to the body of science. Workshop participants defined the population of patients with out-of-hospital cardiac arrest most likely to benefit from ECPR. ECPR-eligible patients include those aged 18 to 75 years functioning independently without comorbidity; before suffering a witnessed out-of-hospital cardiac arrest and without any obvious cause of the cardiac arrest; presenting in a shockable rhythm and transported with mechanical cardiopulmonary resuscitation to an ECPR-capable institute within 30 minutes, which is recommended after 3 rounds of advanced life support treatment without return of spontaneous circulation. There are significant inequities in out-of-hospital cardiac arrest care that need to be addressed such that outcomes are optimized for each target region before implementing ECPR in a clinical or implementation trial.
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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 | 0.259 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".