Extracorporeal Membrane Oxygenation during Respiratory Pandemics: Past, Present, and Future
Bibliographic record
Abstract
The role of extracorporeal membrane oxygenation (ECMO) in the management of severe acute respiratory failure, including acute respiratory distress syndrome, has become better defined in recent years in light of emerging high-quality evidence and technological advances. Use of ECMO has consequently increased throughout many parts of the world. The coronavirus disease (COVID-19) pandemic, however, has highlighted deficiencies in organizational capacity, research capability, knowledge sharing, and resource use. Although governments, medical societies, hospital systems, and clinicians were collectively unprepared for the scope of this pandemic, the use of ECMO, a highly resource-intensive and specialized form of life support, presented specific logistical and ethical challenges. As the pandemic has evolved, there has been greater collaboration in the use of ECMO across centers and regions, together with more robust data reporting through international registries and observational studies. Nevertheless, centralization of ECMO capacity is lacking in many regions of the world, and equitable use of ECMO resources remains uneven. There are no widely available mechanisms to conduct large-scale, rigorous clinical trials in real time. In this critical care review, we outline lessons learned during COVID-19 and prior respiratory pandemics in which ECMO was used, and we describe how we might apply these lessons going forward, both during the ongoing COVID-19 pandemic and in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".