Extracorporeal life support and systemic inflammation
Bibliographic record
Abstract
Extracorporeal life support (ECLS) encompasses a wide range of extracorporeal modalities that offer short- and intermediate-term mechanical support to the failing heart or lung. Apart from the daily use of cardiopulmonary bypass (CPB) in the operating room, there has been a resurgence of interest and utilization of veno-arterial and veno-venous extracorporeal membrane oxygenation (VA- and VV-ECMO, respectively) and extracorporeal carbon dioxide removal (ECCO 2 R) in recent years. This might be attributed to the advancement in technology, nonetheless the morbidity and mortality associated with the clinical application of this technology is still significant. The initiation of ECLS triggers a systemic inflammatory response, which involves the activation of the coagulation cascade, complement systems, endothelial cells, leukocytes, and platelets, thus potentially contributing to morbidity and mortality. This is due to the release of cytokines and other biomarkers of inflammation, which have been associated with multiorgan dysfunction. On the other hand, ECLS can be utilized as a therapy to halt the inflammatory response associated with critical illness and ICU therapeutic intervention, such as facilitating ultra-protective mechanical ventilation. In addition to addressing the impact on outcome of the relationship between inflammation and ECLS, two different but complementary pathophysiological perspectives will be developed in this review: ECLS as the cause of inflammation and ECLS as the treatment of inflammation. This framework may be useful in guiding the development of novel therapeutic strategies to improve the outcome of critical illness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".