Adjuvant and neoadjuvant therapies for hepatocellular carcinoma
Bibliographic record
Abstract
Immune-oncology-based regimens have shown efficacy in advanced HCC and have been implemented as standard of care as first-line therapy. Their efficacy, including high response rates, and safety justify their evaluation in earlier disease stages. Following negative results for adjuvant sorafenib in the global STORM trial in 2015, 4 global phase 3 trials, featuring different immune checkpoint inhibitor combinations, entered in parallel the race in the adjuvant setting. The IMbrave050 trial, comparing adjuvant atezolizumab in combination with bevacizumab to active surveillance following curative-intent resection or ablation, was the first to report, fast-tracking the results of the first interim analysis and demonstrating an improvement in recurrence-free survival. The trial has provoked a discussion on the horizon of expectations from adjuvant treatment and the clinical relevance of efficacy endpoints. Moreover, major pathological responses reported from early phase 2 data in the neoadjuvant setting provide a strong rationale for the evaluation of these concepts in phase 3 trials. In this review, we summarize current evidence and outline future directions for systemic therapies in early-stage HCC.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".