Weaning from venovenous extracorporeal membrane oxygenation for acute respiratory failure: challenges and opportunities
Bibliographic record
Abstract
PURPOSE OF REVIEW: The process of weaning from venovenous extracorporeal membrane oxygenation (V-V ECMO) is a critical step in the recovery of patients with severe acute respiratory distress syndrome (ARDS), yet clinical practice is highly variable and lacks strong evidence-based guidance. This review summarizes the current understanding and emerging data on weaning from V-V ECMO while highlighting key areas for future research. RECENT FINDINGS: While several single-center studies have evaluated structured weaning protocols, no definitive multicenter trial with patient-centered outcomes has been completed. Recent work has highlighted physiologic predictors of successful weaning such as tidal volume, carbon dioxide clearance, and diaphragm function, as well as the challenges posed by ICU-acquired weakness, prolonged ECMO runs, and severe lung injury. Importantly, even patients requiring extended ECMO support can often be weaned successfully, and premature decisions about irreversibility should be avoided. SUMMARY: The optimal strategy for weaning V-V ECMO remains an area of clinical uncertainty. Protocolized approaches, careful physiologic monitoring, and patience may improve outcomes. Ongoing trials and future research will be critical in shaping evidence-based guidelines for liberation from ECMO support.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".