ALBI Grade Enables Risk Stratification for Bleeding Events and Refines Prognostic Prediction in Advanced HCC Following Atezolizumab and Bevacizumab
Bibliographic record
Abstract
Background and Aims: Atezolizumab and bevacizumab (A+B) are recommended for treating unresectable hepatocellular carcinoma (HCC). Although highly effective, A+B can lead to potentially life-threatening adverse events including bleeding. We investigated whether albumin-bilirubin (ALBI) grade identifies patients with a higher risk of bleeding and its impact on prognosis than the Child-Pugh (CP) score. Methods: We performed a multicenter retrospective study of 15 tertiary referral centers that consecutively treated patients with A+B. We analyzed the association between the ALBI grade and gastrointestinal bleeding using the χ2 test. Overall survival (OS) stratified by ALBI was estimated using the Kaplan-Meier method and the predictive value for the 6-months OS landmark with ROC curves. Results: Of the 368 patients included in the analysis, 163 (44.3%), 192 (52.2%) and 13 (3.5%) had ALBI 1, ALBI 2, and ALBI 3, respectively. ALBI grade was associated with a 3-fold increase in bleeding risk (3.1% in ALBI 1 vs 10.2% in ALBI 2/3, p=0.008). Among 192 patients with pre-treatment EGD, G2 and G3 varices were associated with an increased risk of bleeding, whereas G1 varices had a similar risk as no varices. Patients with ALBI 1 achieved a longer median OS (not reached; 95% CI, 24.9-33.7), than ALBI 2 (9.7 months; 95% CI, 7.0-12.3) or ALBI 3 (5.6 months; 95% CI, 0.1-12.0). ALBI outperformed the CP score for predicting 6-month OS with an AUC 0.79 of ALBI versus 0.71 for the CP score (p=0.01). Conclusion: A Higher ALBI grade was associated with an increased risk of gastrointestinal bleeding after receiving A+B, and outperformed the CP score in predicting worse survival.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".