In severe alcohol‐related hepatitis, acute kidney injury is prevalent, associated with mortality independent of liver disease severity, and can be predicted using <scp>IL</scp>‐8 and <scp>micro‐RNAs</scp>
Bibliographic record
Abstract
BACKGROUND: The prevalence, prediction and impact of acute kidney injury (AKI) in alcohol-related hepatitis (AH) is uncertain. AIMS: We aimed to determine AKI incidence; association with mortality; evaluate serum biomarkers and the modifying effects of prednisolone and pentoxifylline in the largest AH cohort to date. METHODS: Participants in the Steroids or Pentoxifylline for Alcoholic Hepatitis trial with day zero (D0) creatinine available were included. AKI was defined by modified International Club of Ascites criteria; incident AKI as day 7 (D7) AKI without D0-AKI. Survival was compared by Kaplan-Meier; mortality associations by Cox regression; associations with AKI by binary logistic regression; biomarkers by AUROC analyses. RESULTS: D0-AKI was present in 198/1051 (19%) participants; incident AKI developed in a further 119/571 (21%) with available data. Participants with D0-AKI had higher 90-day mortality than those without (32% vs. 25%, p = 0.008), as did participants with incident AKI compared to those without D0-AKI or incident AKI (47% vs. 25%, p < 0.001). Incident AKI was associated with D90 mortality adjusted for age and discriminant function (AHR 2.15, 1.56-2.97, p < 0.001); D0-AKI was not. Prednisolone therapy reduced incident AKI (AOR 0.55, 0.36-0.85, p = 0.007) but not mortality. D0 bilirubin and IL-8 combined, miR-6826-5p, and miR-6811-3p predicted incident AKI (AUROCs 0.726, 0.821, 0.770, p < 0.01). CONCLUSIONS: Incident AKI is associated with 90-day mortality independent of liver function. Prednisolone therapy was associated with reduced incident AKI. IL-8 and several miRNAs are potential biomarkers to predict AKI. Novel therapies to prevent incident AKI should be evaluated in AH to reduce mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".