Risk Stratification Based on Chronic Liver Failure Consortium Acute Decompensation Score in Patients With Child‐Pugh B Cirrhosis and Acute Variceal Bleeding
Bibliographic record
Abstract
BACKGROUND AND AIMS: Optimal candidates for early transjugular intrahepatic portosystemic shunt (TIPS) in patients with Child-Pugh B cirrhosis and acute variceal bleeding (AVB) remain unclear. This study aimed to test the hypothesis that risk stratification using the Chronic Liver Failure Consortium Acute Decompensation score (CLIF-C ADs) may be useful to identify a subgroup at high risk of mortality or further bleeding that may benefit from early TIPS in patients with Child-Pugh B cirrhosis and AVB. APPROACH AND RESULTS: We analyzed the pooled individual data from two previous studies of 608 patients with Child-Pugh B cirrhosis and AVB who received standard treatment between 2010 and 2017 in China. The concordance index values of CLIF-C ADs for 6-week and 1-year mortality (0.715 and 0.708) were significantly better than those of active bleeding at endoscopy (0.633 [P < 0.001] and 0.556 [P < 0.001]) and other prognostic models. With X-tile software identifying an optimal cutoff value, patients were categorized as low risk (CLIF-C ADs <48), intermediate risk (CLIF-C ADs 48-56), and high risk (CLIF-C ADs >56), with a 5.6%, 16.8%, and 25.4% risk of 6-week death, respectively. Nevertheless, the performance of CLIF-C ADs for predicting a composite endpoint of 6-week death or further bleeding was not satisfactory (area under the receiver operating characteristics curve [AUC], 0.588). A nomogram incorporating components of CLIF-C ADs and albumin, platelet, active bleeding, and ascites significantly improved the prediction accuracy (AUC, 0.725). CONCLUSIONS: In patients with Child-Pugh B cirrhosis and AVB, risk stratification using CLIF-C ADs identifies a subgroup with high risk of death that may derive survival benefit from early TIPS. With improved prediction accuracy for 6-week death or further bleeding, the data-driven nomogram may help to stratify patients in randomized trials. Future external validation of these findings in patients with different etiologies is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".