Extracorporeal life support and systemic inflammation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (ECCO2R) 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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it