Coupled Plasma Filtration Adsorption: Rationale, Technical Development and Early Clinical Experience
Bibliographic record
Abstract
The adjuvant treatment of sepsis remains a major therapeutic challenge. Blood purification is theoretically appealing if the humoral theory of sepsis is accepted as the basis for intervention. In this setting, blood purification would provide a broad-based restoration of humoral homeostasis thereby avoiding both excessive inflammation and counterinflammation. Several techniques of blood purification have been tried or are under active investigation. One of these is the so-called coupled plasma filtration adsorption (CPFA). CPFA is a novel extracorporeal blood purification therapy aimed at nonselectively reducing the circulating levels and activities of both pro- and anti-inflammatory mediators during sepsis and multiorgan failure. In vitro studies have shown CPFA to be effective in binding a broad range of such mediators proving its technical efficacy. Subsequent animal models have shown a beneficial effect on survival in endotoxemia. These studies have provided the necessary technical developments and biologic rationale for initial human studies. Two phase I/IIa clinical studies have now been performed. Both studies have shown that CPFA improves blood pressure and restores immune function in patients with severe sepsis and multiorgan dysfunction. In this article, we will discuss some of the basic principles involved in sorbent technology, and how these may contribute to treatment efficacy, review animal experiments with CPFA and finally discuss the results of recent human studies and their implications.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".