Pretreatment Modified Albumin–Bilirubin Grade Is an Important Predictive Factor Associated with the Therapeutic Response and the Continuation of Atezolizumab plus Bevacizumab Combination Therapy for Patients with Unresectable Hepatocellular Carcinoma
Bibliographic record
Abstract
Background: Atezolizumab plus bevacizumab (ATZ + BV) treatment is recommended as the first-line systemic therapy for patients with unresectable hepatocellular carcinoma (u-HCC). This study aimed to investigate the predictive factors of therapeutic response and the continuation of ATZ + BV treatment for u-HCC in a real-world setting. Methods: This retrospective study was conducted between January 2021 and April 2022. Twenty-eight patients with u-HCC, who were treated with ATZ + BV, were assessed for their treatment response, continuation, and adverse events (AEs). Results: Among the 28 patients, 24 were evaluated at the first imaging. The objective response rate (ORR) was 29.2% (n = 7), and 54.2% (n = 13) on the response evaluation criteria in solid tumors (RECIST 1.1) and in the modified RECIST (mRECIST) guidelines, respectively. Comparing the objective response (OR) group (n = 13) and the non-OR group (n = 11), the modified albumin–bilirubin (mALBI) grades 1 and 2a were found to be significant predictive factors for OR (p = 0.021) in the mRECIST guidelines. Among the 28 patients, 17 discontinued their treatment due to AEs. Comparing the treatment continuation (n = 11) and discontinuation groups (n = 17), a Child–Pugh score of five points (p = 0.009) and mALBI grades 1 and 2a (p = 0.020) were predictive factors with significant differences. Conclusions: Pretreatment mALBI grades 1 and 2a were the important predictive factors associated with the therapeutic response and the therapeutic continuation of ATZ + BV for patients with u-HCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".