Advances in management and prognostication in critically ill cirrhotic patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide an update on the recent publications for the management and prognostication of critically ill cirrhotic patients before and after liver transplant. RECENT FINDINGS: The CLIF Acute-oN-ChrONicLIver Failure in Cirrhosis (CANONIC) study recently derived an evidence-based definition of acute-on-chronic liver failure (ACLF): hepatic decompensation; organ failure [predefined by the Chronic Liver Failure-Sequential Organ Failure Assessment (CLIF-SOFA)]; and high 28-day mortality rate. Although Sequential Organ Failure Assessment (SOFA) appears to be more accurate in predicting ICU and hospital mortality in ACLF patients, CLIF-SOFA has been derived specifically for critically ill cirrhotic patients, including those not receiving mechanical ventilation. Recent data suggest that a lower transfusion target in esophageal variceal bleeding (<7 g/l) is safe. Newly defined 'cirrhosis-associated acute kidney injury (AKI)' correlates with mortality, organ failure and length of hospital stay. Although the SOFA score appears to perform better than liver-specific scoring systems [Model for End-stage Liver Disease (MELD) and Child-Pugh scores], neither MELD nor SOFA appears to independently predict posttransplant survival; however, correlated with lengths of ICU and hospital stay. For patients declined for liver transplant, palliative care referral and appropriate goals of care are rarely achieved. SUMMARY: New definitions for ACLF, cirrhosis-associated AKI and the CLIF-SOFA may improve the discrimination between survivors and nonsurvivors with ACLF. Predicting futility postliver transplant based on preliver transplant severity of illness still poses significant challenges.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".