Potential Molecular Targeted Therapy for Unresectable Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers, representing a serious worldwide health concern. The recurrence incidence of hepatocellular carcinoma (HCC) following surgery or ablation is as high as 70%. Thus, the clinical applicability of standard surgery and other locoregional therapy to improve the outcomes of advanced HCC is restricted and far from ideal. The registered trials did not identify a treatment that prolonged recurrence-free survival, the primary outcome of the majority of research. Several investigator-initiated trials have demonstrated that various treatments extend patients' recurrence-free or overall survival after curative therapies. In the past decade, targeted therapy has made significant strides in the treatment of advanced HCC. These targeted medicines produce antitumour effects via specific signals, such as anti-angiogenesis or advancement of the cell cycle. As a typical systemic treatment option, it significantly improves the prognosis of this fatal disease. In addition, the combination of targeted therapy with an immune checkpoint inhibitor is redefining the paradigm of advanced HCC treatment. In this review, we focused on the role of approved targeted medicines and potential therapeutic targets in unresectable 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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".