Extracorporeal Blood Purification and Organ Support in the Critically Ill Patient during COVID-19 Pandemic: Expert Review and Recommendation
Bibliographic record
Abstract
Critically ill COVID-19 patients are generally admitted to the ICU for respiratory insufficiency which can evolve into a multiple-organ dysfunction syndrome requiring extracorporeal organ support. Ongoing advances in technology and science and progress in information technology support the development of integrated multi-organ support platforms for personalized treatment according to the changing needs of the patient. Based on pathophysiological derangements observed in COVID-19 patients, a rationale emerges for sequential extracorporeal therapies designed to remove inflammatory mediators and support different organ systems. In the absence of vaccines or direct therapy for COVID-19, extracorporeal therapies could represent an option to prevent organ failure and improve survival. The enormous demand in care for COVID-19 patients requires an immediate response from the scientific community. Thus, a detailed review of the available technology is provided by experts followed by a series of recommendation based on current experience and opinions, while waiting for generation of robust evidence from trials.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".