Mechanical Ventilation for Acute Respiratory Distress Syndrome during Extracorporeal Life Support. Research and Practice
Bibliographic record
Abstract
Abstract Ventilator-induced lung injury remains a key contributor to the morbidity and mortality of acute respiratory distress syndrome (ARDS). Efforts to minimize this injury are typically limited by the need to preserve adequate gas exchange. In the most severe forms of the syndrome, extracorporeal life support is increasingly being deployed for severe hypoxemia or hypercapnic acidosis refractory to conventional ventilator management strategies. Data from a recent randomized controlled trial, a post hoc analysis of that trial, a meta-analysis, and a large international multicenter observational study suggest that extracorporeal life support, when combined with lower Vt and airway pressures than the current standard of care, may improve outcomes compared with conventional management in patients with the most severe forms of ARDS. These findings raise important questions not only about the optimal ventilation strategies for patients receiving extracorporeal support but also regarding how various mechanisms of lung injury in ARDS may potentially be mitigated by ultra–lung-protective ventilation strategies when gas exchange is sufficiently managed with the extracorporeal circuit. Additional studies are needed to more precisely delineate the best strategies for optimizing invasive mechanical ventilation in this patient population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".