Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) is a common complication of advanced cirrhosis. Type 1 hepatorenal syndrome is the best-known and most severe form of AKI, and it has a precise definition and a set of specific diagnostic criteria. More recently, it has become recognized that milder degrees of renal dysfunction also have a negative impact on patient outcome in various patient populations. Key Messages: Several definitions and criteria for staging the severity of AKI have been proposed, including the RIFLE (Risk, Injury, Failure, Loss of Function and End-Stage Renal Disease) group, the Acute Kidney Injury Network (AKIN), and the Kidney Disease: Improving Global Outcome (KDIGO) group. All of them incorporate some changes of serum creatinine and urine output in the definition and staging of AKI. The hepatology community has mostly embraced the AKIN diagnostic and staging criteria and has applied them in the prognostication of patients with advanced cirrhosis. However, the AKIN criteria have not been strictly applied in all studies on cirrhosis. This is partly related to the fact that changes in urine output are difficult to assess in advanced cirrhosis, and partly related to the difficulty in defining the baseline serum creatinine from which the change in serum creatinine is calculated. This has led to some confusion in the interpretation of results of the various studies on AKI in cirrhosis. More recently, some investigators have suggested incorporating the AKIN criteria with setting a lower limit of serum creatinine of 1.5 mg/dl in determining the diagnosis and prognosis of AKI in cirrhosis. CONCLUSIONS: This is an ongoing debate as to how best to define AKI in cirrhosis. In the near future there should be prospective clinical trials that will clarify which diagnostic and staging criteria of AKI will best serve the cirrhotic population.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".