Real world eligibility for cabozantinib (C), regorafenib (Reg), and ramucirumab (Ram) in hepatocellular carcinoma (HCC) patients after sorafenib (S).
Bibliographic record
Abstract
422 Background: The CELESTIAL, RESORCE, and REACH-2 trials showed survival benefit of C, Reg, and Ram, respectively, when given after S to HCC patients. However, strict eligibility criteria (SEC) may limit generalizability. In clinical practice, modified eligibility criteria (MEC) may be used to offer treatments to select patients with slightly worse performance status (ECOG 2) or limited liver dysfunction (Child-Pugh (CP) B7). This study evaluated which patients in the real world would be eligible for these new treatments using SEC and MEC, and their prognostic impact. Methods: HCC patients who received S between 01/2008-06/2017 in British Columbia, Alberta, Princess Margaret Cancer Centre, and Sunnybrook Cancer Centre in Canada were included. Clinical, pathologic, laboratory and outcome data were collected. Patients were classified as eligible or ineligible based on available CELESTIAL, RESORCE, REACH-2 clinical trial SEC or MEC. Median overall survival (mOS) for these groups was assessed using the Kaplan-Meier method. Results: A total of 730 patients were identified. Using SEC, only 13.1% of patients would be eligible for C, Reg, or Ram (table). Expanding eligibility to include patients who meet MEC increased the proportion of eligible patients to 31.7%. Patients who met SEC had longer mOS compared to those who were ineligible. The most common reasons for not meeting SEC across all 3 trials were ECOG ≥ 2 (61.7%) and CP ≥ B (63.9%). Higher ineligibility for Reg or Ram was likely driven by strict trial-specific criteria, with 28.0% of patients ineligible for Reg due to S intolerance and 58.9% ineligible for Ram due to AFP < 400. Conclusions: Only a small proportion of real-world HCC patients would be eligible for C, Reg, or Ram based on SEC. More than twice as many patients would likely receive treatment if MEC were applied. If MEC are adopted, ongoing real-world evidence generation will be important to evaluate outcomes in these unstudied patient groups. [Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".