Real-World, Observational, Retrospective Study to Evaluate the Effectiveness and Safety of Treatment with Sorafenib in Patients with Advanced Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) accounts for approximately 90% of liver cancer cases. Sorafenib, the first drug to demonstrate survival benefits for advanced HCC, was validated through the SHARP randomized clinical trial (RCT). While RCTs are essential for assessing new therapies, real-world studies provide additional insights into their effectiveness in routine clinical practice. This study aimed to evaluate sorafenib's real-world effectiveness by analyzing overall survival (OS) and the time to radiological and symptomatic progression. METHODS: Data from 368 patients treated with sorafenib at a Brazilian Cancer Center between 2009 and 2020 were retrospectively reviewed. RESULTS: The median OS was 9.6 months, and the time to radiological progression was 5.3 months, similar to the SHARP trial. However, the time to symptomatic progression was shorter (2.3 months) than the SHARP study (4.1 months). In terms of safety, 27.4% of patients presented clinically relevant toxicities, and 24.5% needed to discontinue treatment due to toxicity. CONCLUSIONS: Overall, sorafenib demonstrated effectiveness in the studied population, with OS and radiological progression times comparable to SHARP study results. The difference in symptomatic progression may be due to the study's retrospective nature and limitations.
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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 | 0.003 | 0.008 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".