Practice Patterns and Ethical Considerations in the Management of Venovenous Extracorporeal Membrane Oxygenation Patients: An International Survey*
Bibliographic record
Abstract
OBJECTIVES: To characterize physicians' practices and attitudes toward the initiation, limitation, and withdrawal of venovenous extracorporeal membrane oxygenation for severe respiratory failure and evaluate factors associated with these attitudes. DESIGN: Electronic, cross-sectional, scenario-based survey. SETTING: Extracorporeal membrane oxygenation centers affiliated with the Extracorporeal Life Support Organization and the International Extracorporeal Membrane Oxygenation Network. SUBJECTS: Attending-level physicians with experience managing adult patients receiving venovenous extracorporeal membrane oxygenation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Five-hundred thirty-nine physicians in 39 countries across six continents completed the survey. Factors that influenced the decision to limit extracorporeal membrane oxygenation initiation included older patient age (46.9%), additional organ failures (37.7%), and prolonged mechanical ventilation (35.1%). Patient comorbidities (70.5%), patient's wishes (56.0%), and etiology of respiratory failure (37.7%) were factors that influenced the decision to withdraw extracorporeal membrane oxygenation. In multivariable analysis, factors associated with increased odds of withdrawing life-sustaining therapies included pulmonary fibrosis, stroke, surrogate's desire to withdraw, lack of knowledge regarding patient's or surrogate's wishes in the setting of fibrosis, not initiating extracorporeal membrane oxygenation in the baseline scenario, and respondent religiosity. Factors associated with decreased odds of withdrawal included practicing in an environment where it is not legally possible to make decisions against patient or surrogate wishes. Most respondents (90.5%) involved other physicians in treatment decisions for extracorporeal membrane oxygenation patients, whereas only 53.2%, 45.3%, and 29.5% of respondents involved surrogates, awake patients, or bedside nurses, respectively. CONCLUSIONS: Patient and physician-level factors were associated with decision-making regarding extracorporeal membrane oxygenation initiation and withdrawal, including patient prognosis and knowledge of patient or surrogate wishes. Respondents reported low rates of engaging in shared decision-making when managing patients receiving extracorporeal membrane oxygenation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".