Design and Endpoints of Clinical Trials in Hepatocellular Carcinoma
Bibliographic record
Abstract
The design of clinical trials in hepatocellular carcinoma (HCC) is complex because many patients have concurrent liver disease, which can confound the assessment of clinical benefit. There is an urgent need for high-quality trials in this disease. An expert panel was convened by the American Association for the Study of Liver Diseases to develop guidelines that provide a common framework for designing trials to facilitate comparability of results. According to these guidelines, randomized phase 2 trials with a time-to-event primary endpoint, such as time to progression, are pivotal in clinical research on HCC. Survival remains the main endpoint to measure effectiveness in phase 3 studies, whereas time to recurrence is proposed as an appropriate endpoint in the adjuvant setting. Because progression-free survival and disease-free survival are composite endpoints, they are more vulnerable than others in HCC clinical studies and may not be able to capture clinical benefits. Selection of the target population should be based on the Barcelona Clinic Liver Cancer staging system. New drugs should be tested in patients with well-preserved liver function (Child-Pugh A class). Patients assigned to the control arm should receive standard-of-care therapy, that is, chemoembolization for patients with intermediate-stage disease and sorafenib for patients with advanced-stage disease. Further research is needed to incorporate biomarkers and molecular imaging into clinical research in HCC. These surrogate markers may help to enrich study populations and maximize the cost-benefit ratio of trial execution. Design and conduct of phase 3 trials should be coordinated by centers with appropriate expertise in 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 | 0.327 | 0.341 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".