Acute kidney injury in cirrhosis: implications for liver transplantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Acute kidney injury (AKI) in cirrhosis consists of varying phenotypes, with hepatorenal syndrome (HRS) representing a single entity. Prompt recognition and diagnosis of AKI cause identifies appropriate therapeutic measures. This review provides an overview of AKI definitions, highlights challenges in quantifying renal impairment in cirrhosis, lists novel diagnostic AKI biomarkers, and summarizes transplantation implications. RECENT FINDINGS: Biomarkers (neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, interleukin-18, and liver-type fatty acid-binding protein) may assist in the identification of underlying acute tubular necrosis. Of these, neutrophil gelatinase-associated lipocalin is the most promising; however, significant overlap occurs among AKI phenotypes, with diagnostic values yet to be defined. Mainstay treatment of HRS consists of albumin and vasopressors. Acute-on-chronic liver failure grade independently predicts response to terlipressin treatment. Many end-stage liver disease patients with AKI have underlying chronic kidney disease with important implications on pre and postliver transplantation mortality. Simultaneous liver-kidney transplant candidacy is based on low likelihood of renal recovery. SUMMARY: Novel biomarkers may assist in identification of acute tubular necrosis and persistent/severe AKI. Norepinephrine has been suggested to be inferior to terlipressin, with additional research required. Increasing acute-on-chronic liver failure grade correlates with lower likelihood of vasopressor response in HRS. Severe preliver transplantation AKI confers significantly worse postliver transplantation renal outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".