Hemodynamic Aspects of Veno-Arterial Extracorporeal Membrane Oxygenation for Cardiac Support: A Worldwide Survey
Bibliographic record
Abstract
There is limited data available to guide management of patients supported with veno-arterial extracorporeal membrane oxygenation (VA-ECMO). An international cross-sectional survey of medical directors/program coordinators from Extracorporeal Life Support Organization centers was conducted. A hierarchical clustering on principle components was used. A total of 243 (55%) centers responded and were divided into three clusters: Cluster 1 (n = 102) had few high volumes and low specialized heart failure (HF) involvement; Cluster 2 (n = 75) had few high volumes and moderate HF involvement; Cluster 3 (n = 66) contained the majority of centers with >50 annual cases and high HF involvement. The most divergent responses were observed between Clusters 1 and 3 wherein Cluster 1 centers were less likely to change management based on pulse pressure (77% vs. 100%; p < 0.001) and would rather avoid inotropes to "rest the heart" (28%). Cluster 3 centers were more likely to perform daily echocardiograms (50% vs. 24%, p < 0.001), which were less likely to be exclusively performed by cardiologist (36% vs. 58%, p < 0.046) and base weaning on echocardiographic findings, when compared to Cluster 1 (3.97/5 vs. 3.56, p < 0.001). Responses were variable in management reflecting the lack of evidence for hemodynamic care for those supported with VA-ECMO.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".