Mechanical Ventilation during Extracorporeal Membrane Oxygenation. An International Survey
Bibliographic record
Abstract
RATIONALE: In patients with severe, acute respiratory failure undergoing venovenous extracorporeal membrane oxygenation (VV-ECMO), the optimal strategy for mechanical ventilation is unclear. OBJECTIVES: Our objective was to describe ventilation practices used in centers registered with the Extracorporeal Life Support Organization (ELSO). METHODS: We conducted an international cross-sectional survey of medical directors and ECMO program coordinators from all ELSO-registered centers. The survey was distributed using a commercial website that collected information on center characteristics, the presence of a mechanical ventilator protocol, ventilator settings, and weaning practices. E-mails were sent out to medical directors or coordinators at each ELSO center and their responses were pooled for analysis. MEASUREMENTS AND MAIN RESULTS: We analyzed 141 (50%) individual responses from the 283 centers contacted across 28 countries. Only 27% of centers reported having an explicit mechanical ventilation protocol for ECMO patients. The majority of these centers (77%) reported "lung rest" to be the primary goal of mechanical ventilation, whereas 9% reported "lung recruitment" to be their ventilation strategy. A tidal volume of 6 ml/kg or less was targeted by 76% of respondents, and 58% targeted a positive end-expiratory pressure of 6-10 cm H2O while ventilating patients on VV-ECMO. Centers prioritized weaning VV-ECMO before mechanical ventilation. CONCLUSIONS: Although ventilation practices in patients supported by VV-ECMO vary across ELSO centers internationally, the majority of centers used a strategy that targeted lung-protective thresholds and prioritized weaning VV-ECMO over mechanical ventilation.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 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".