The Prediction of In‐Hospital Mortality in Decompensated Cirrhosis with Acute‐on‐Chronic Liver Failure
Bibliographic record
Abstract
Acute-on-chronic liver failure (ACLF) is a condition in cirrhosis associated with organ failure (OF) and high short-term mortality. Both the European Association for the Study of the Liver-Chronic Liver Failure (EASL-CLIF) and North American Consortium for the Study of End-Stage Liver Disease (NACSELD) ACLF definitions have been shown to predict ACLF prognosis. The aim of this study was to compare the ability of the EASL-CLIF versus NACSELD systems over baseline clinical and laboratory parameters in the prediction of in-hospital mortality in admitted patients with decompensated cirrhosis. Five NACSELD centers prospectively collected data to calculate EASL-CLIF and NACSELD-ACLF scores for admitted patients with cirrhosis who were followed for the development of OF, hospital course, and survival. Both the number of OFs and the ACLF grade or presence were used to determine the impact of NACSELD versus EASL-CLIF definitions of ACLF above baseline parameters on in-hospital mortality. A total of 1031 patients with decompensated cirrhosis (age, 57 ± 11 years; male, 66%; Child-Pugh-Turcotte score, 10 ± 2; Model for End-Stage Liver Disease [MELD] score, 20 ± 8) were enrolled. Renal failure prevalence (28% versus 9%, P < 0.001) was more common using the EASL-CLIF versus NACSELD definition, but the prevalence rates for brain, circulatory, and respiratory failures were similar. Baseline parameters including age, white cell count on admission, and MELD score reasonably predicted in-hospital mortality (area under the curve, 0.76). The addition of number of OFs according to either system did not improve the predictive power of the baseline parameters for in-hospital mortality, but the presence of NACSELD-ACLF did. However, neither system was better than baseline parameters in the prediction of 30- or 90-day outcomes. The presence of NACSELD-ACLF is equally effective as the EASL-CLIF ACLF grade, and better than baseline parameters in the prediction of in-hospital mortality in patients with cirrhosis, but not superior in the prediction of longer-term 30- or 90-day outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".