Invasive mechanical ventilation in patients with acute respiratory distress syndrome receiving extracorporeal support: a narrative review of strategies to mitigate lung injury
Bibliographic record
Abstract
Veno-venous extracorporeal membrane oxygenation is indicated in patients with acute respiratory distress syndrome and severely impaired gas exchange despite evidence-based lung protective ventilation, prone positioning and other parts of the standard algorithm for treating such patients. Extracorporeal support can facilitate ultra-lung-protective ventilation, meaning even lower volumes and pressures than standard lung-protective ventilation, by directly removing carbon dioxide in patients needing injurious ventilator settings to maintain sufficient gas exchange. Injurious ventilation results in ventilator-induced lung injury, which is one of the main determinants of mortality in acute respiratory distress syndrome. Marked reductions in the intensity of ventilation to the lowest tolerable levels under extracorporeal support may be achieved and could thereby potentially mitigate ventilator-induced lung injury and theoretically patient self-inflicted lung injury in spontaneously breathing patients with high respiratory drive. However, the benefits of this strategy may be counterbalanced by the use of continuous deep sedation and even neuromuscular blocking drugs, which may impair physical rehabilitation and impact long-term outcomes. There are currently a lack of large-scale prospective data to inform optimal invasive ventilation practices and how to best apply a holistic approach to patients receiving veno-venous extracorporeal membrane oxygenation, while minimising ventilator-induced and patient self-inflicted lung injury. We aimed to review the literature relating to invasive ventilation strategies in patients with acute respiratory distress syndrome receiving extracorporeal support and discuss personalised ventilation approaches and the potential role of adjunctive therapies in facilitating lung protection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".