Sorafenib Improves Survival in Metastatic Hepatocellular Carcinoma: A Case Report
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a very common cancer. Curative treatments and local ones are well validated. Sorafenib, a multi-kinase receptor inhibitor was introduced in 2007 for advanced HCC in patients with preserved liver function. HCC is known to be resistant to systemic chemotherapy, and there are no validated therapies improving survival for metastatic disease. Herein, we report a case of a 45 years old woman with chronic hepatitis B infection submitted to a right hepatectomy in May 2001 for an hepatic tumor with more than 10 cm wide, confirmed as a HCC moderately differentiated. Three years later, a solitary pulmonary metastasis was documented and a metastectomy was done. In February 2009, the patient started on sorafenib 400 mg twice daily due to an inferior mediastinal metastasis with a vena cava thrombus associated. Computed tomography (CT) scan done 13 months after revealed a consistently mass reduction in more than 50% and a clinically well patient without important collateral effects. HCC is a highly vascularized tumor and sorafenib is known to inhibit both tumor angiogenesis and tumor cell survival. It is already approved for the treatment of advanced and metastatic renal cell cancer. In our case, the combination of two well done surgical procedures and the posterior use of sorafenib when a metastasis was found in an inaccessible surgical place with macroscopic vascular invasion, led to a long survival without important side effects.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".