Variability in Venovenous Extracorporeal Membrane Oxygenation Candidacy Decision-Making: An International Survey
Bibliographic record
Abstract
OBJECTIVE: To characterize the variability in venovenous extracorporeal membrane oxygenation (VV-ECMO) candidacy decision-making processes across international Extracorporeal Life Support Organization (ELSO) member institutions. DESIGN: An international survey study of ELSO centers performing adult VV-ECMO. SETTING: Internet-based survey conducted between February 2024 and April 2024. PARTICIPANTS: ECMO clinicians representing ELSO member institutions, including ECMO directors, physicians, coordinators, and others listed in the ELSO institutional directory as of January 2024. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Measurements included center characteristics, decision-making processes, contraindications used, and clinician perceptions of consistency in candidacy determinations. Most centers (82%) reported having formal inclusion and exclusion criteria, with 95% having absolute contraindications to initiating VV-ECMO as a bridge to recovery. However, very few centers shared identical sets of contraindications. The most common absolute contraindications were severe neurologic injury (77%) and disseminated malignancy (75%). Clinician judgment was perceived as equally or more important than institutional guidelines in 93% of centers. Representatives from 54% of centers believed that candidacy decisions were not always consistent between clinically identical patients, and less than half of centers routinely reviewed all prior candidacy decisions. CONCLUSIONS: This study reveals significant variability in VV-ECMO candidacy decision-making processes across international ELSO centers. The observed inconsistencies in contraindications, reliance on clinical judgment, and perceived variability in decisions suggest a need for more standardized, evidence-based approaches to ECMO candidacy determination. Implementing routine review processes and developing more robust guidelines could improve consistency and equity in ECMO allocation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".