Combining Immune Checkpoint Inhibitors with Loco-Regional Treatments in Hepatocellular Carcinoma: Ready for Prime Time?
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a disease with a poor prognosis, often diagnosed at an advanced stage. Therapeutic options have developed considerably in recent years, particularly with trans-arterial treatments. Systemic treatments have also evolved significantly, with the rise of immune checkpoint inhibitors (ICI) as first-line treatment for advanced HCC. The combination of loco-regional treatments and ICI is opening up new prospects and is the subject of numerous clinical trials. Recently, two global phase 3 trials investigating ICI-based adjuvant combinations have demonstrated improvements in recurrence-free survival or progression-free survival in patients treated with resection, ablation, or trans-arterial chemoembolization. However, mature data and overall survival results are still awaited but will be difficult to interpret. We are at the start of a new era of combinations of loco-regional treatments and immunotherapy. The identification of the best therapeutic strategies and predictive biomarkers is a crucial issue for future standards in clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".