Medical treatment of unresectable hepatocellular carcinoma: Going beyond sorafenib
Bibliographic record
Abstract
Even though Sorafenib has radically changed the natural history of those hepatocellular carcinoma patients who are not amenable for curative treatments, further therapeutic improvements are badly needed. As it was for Sorafenib, our increasingly refined understanding of the complex mechanisms underlying HCC carcinogenesis are the starting point for the future development of such treatments. Presently, a number of molecularly targeted agents are in different stages of development for this once orphan cancer. Indeed, several pathways are presently being explored to identify potentially active drugs, including epidermal growth factor receptor, vascular endothelial growth factor/vascular endothelial growth factor receptors, mammalian target of rapamycin, phosphatidyl-inositol-3-kinase/Akt, insulin growth factor, Aurora kinase, Wnt/β-catenin, retinoic acid receptor and hepatocyte growth factor/C-Met. This review is aimed at addressing the results obtained so far with these newer drugs, also considering the challenges we shall face in the near future, including the issue of response evaluation and identification of predictive/prognostic biomarkers.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".