Development and validation of a novel prognostic model to predict 1-year post-transplant mortality for acute-on-chronic hepatitis B liver failure: a nationwide, multicentre, cohort study
Bibliographic record
Abstract
Background: Liver transplantation (LT) provides a potential cure for hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). We aimed to develop and externally validate a prognostic model to predict 1-year post-LT mortality in patients with HBV-ACLF. Methods: < 0.05 were entered into the least absolute shrinkage and selection operator (Lasso) analysis for further feature selection. 10-fold cross validation was used to choose the optimal lambda (penalty for the number of features) of the Lasso model. Multivariable Cox regression was applied to construct the HALT model based on the risk factors selected by Lasso analysis. Primary outcome was survival rate at 1-year after LT. Secondary outcomes were short-term (28- and 90-day) and long-term survival after LT (3- and 5-year). Model performance was compared with eight other models (COSSH-ACLF II, COSSH-ACLF, CLIF-C ACLF, AARC, MELD, MELD-Na, SALT-M and TAM scores), using receiver operating characteristic curve and C-index values. A nomogram was developed to analyse the probability of the primary outcome in different graft-recipient combinations based on recipient factors (age, number of organ failures [OF], lactate) and graft factors (donation after circulatory death [DCD] and cold ischaemia time [CIT]). Findings: < 0.001). If the sickest patients (age >55 years, OFs ≥3 and lactate ≥2.5 mmol/L) received high-risk grafts (DCD and CIT >10 h), the estimated 1-year post-LT mortality was 85.6%. Interpretation: The HALT model showed superior predictive ability over eight current models and may help for LT candidate selection and optimal organ allocation. Though the findings need to be verified in prospective studies and among different patient populations. Funding: This work was supported by grants from the National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and the Research Project of Jinan Microecological Biomedicine Shandong Laboratory.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".