Extracorporeal Blood Purification in Sepsis and Sepsis-Related Acute Kidney Injury
Bibliographic record
Abstract
Sepsis-related acute kidney injury (AKI) is an important complicating feature of sepsis, and is associated with greater complexity of care and higher mortality. Until recently, AKI lacked a standard, widely accepted definition, rendering it difficult to compare previously published strategies to prevent, recognize and treat this entity. Recently, the RIFLE classification of AKI has been developed, and confirmed in observational studies to be associated with subsequent morbidity and mortality. The management of sepsis-related AKI is evolving with new basic discoveries and ongoing translational clinical research, and will likely include nephroprotective strategies to protect kidneys in patients at risk, early recognition and amelioration of renal damage and pharmacological interventions to minimize injury and promote recovery. Furthermore, extracorporeal blood purification (EBP) has an important role to play, not only in the replacement of certain aspects of renal organ function such as acid-base/electrolyte homeostasis and extracellular fluid volume, but also in an immunomodulatory fashion. As a therapy that has the potential to influence the course of disease in sepsis, EBP in sepsis and sepsis-related AKI is the subject of this review.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".