Un‐precipitated acute kidney injury is uncommon among stable patients with cirrhosis and ascites
Bibliographic record
Abstract
BACKGROUND & AIMS: Acute episodes of renal dysfunction or acute kidney injury (AKI) in cirrhotic patients with ascites are mostly precipitated by an acute event. The prevalence of un-precipitated AKI in stable ascitic cirrhotic patients is unknown. The aims of this study were to determine (i) the prevalence of un-precipitated AKI in stable cirrhotics with ascites and (ii) any predictive factors for its development. METHODS: A total of 1115 stable cirrhotic patients with mild liver and renal dysfunction but varying degrees of ascites severity from 3 previous satavaptan vs placebo randomized controlled trials (Group A, ascites requiring diuretics but not paracentesis; Group B, ascites requiring frequent paracentesis; Group C, refractory ascites) were included. AKI was diagnosed when there was either an increase of ≥0.3 mg/dL in ≤48 hours or a 50% increase in serum creatinine, and staged according to the fold increase of the serum creatinine. Two serum creatinine levels measured maximally 7 days apart at screening and at randomization of the satavaptan studies with no acute intervening events were used. RESULTS: The prevalence of un-precipitated AKI was 1.8% overall, with the prevalence rising with increasing severity of ascites. Ninety-five per cent of cases were stage 1, with 15% progression rate, 3 reaching the severity of type 1 acute hepatorenal syndrome. Ascites severity was the most powerful predictor for un-precipitated AKI development, which did not predict overall mortality. CONCLUSIONS: Increased prevalence of AKI with more severe ascites despite minimal baseline liver and renal dysfunction suggests that frequent monitoring of renal function in these patients is mandatory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".