LIVER-R study protocol: a global real-world study of durvalumab-based regimens in patients with hepatobiliary cancers
Bibliographic record
Abstract
BACKGROUND: Durvalumab-based regimens have improved outcomes compared with standard of care in unresectable hepatocellular carcinoma (uHCC) and advanced biliary tract cancer (aBTC) clinical trials. Here we describe the protocol for the LIVER-R study, which will evaluate long-term outcomes with durvalumab-based regimens in patients with hepatobiliary cancers in real-world settings. METHODS: LIVER-R (NCT06252753) is an observational study aiming to initially enroll ~2500 adults with uHCC or aBTC from ~179 sites in 22 countries. Patients treated with durvalumab-based regimens as part of routine clinical practice or a global early access program will be included. The primary outcome is overall survival. Secondary outcomes include duration of treatment, progression-free survival, treatment patterns and safety. Data will be collected at baseline and every 6 months. The study will include a baseline period of up to 5 years before index date (initiation of first-line durvalumab-based regimen) and a follow-up period from index until death, loss to follow-up, withdrawal, or study end. Study variables will be analyzed descriptively; time-to-event outcomes will be analyzed using the Kaplan-Meier method. CONCLUSIONS: LIVER-R will produce a large, global, real-world standardized dataset of patients with uHCC or aBTC treated with a durvalumab-based regimen, providing insights into real-world clinical practice. CLINICAL TRIAL REGISTRATION: www.clinicaltrials.gov identifier is NCT06252753.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".