Eligibility for second-line therapy in patients with advanced hepatocellular carcinoma (aHCC): A BC Cancer population-based study.
Bibliographic record
Abstract
491 Background: Evidence supporting second-line therapies has become available for aHCC, including regorafenib, cabozantinib, ramucirumab, and nivolumab. The optimal second-line treatment regimen is unknown, and there remains limited real-world data about the eligibility of patients for second-line therapies in aHCC. We aimed to characterize the real-world eligibility and use of second-line therapies post sorafenib. Methods: We identified all patients with aHCC who received ≥1 cycle of first-line sorafenib between January 1, 2014 and December 31, 2017 across 6 centers in British Columbia (BC), Canada. All patients were required to be Child-Pugh class A for initiation of sorafenib in BC. Baseline characteristics and clinical outcomes were reviewed. Eligibility for second-line therapy was determined using the RESORCE and CELESTIAL study entry criteria. Results: Of 144 patients with advanced HCC who received ≥1 cycle of first-line sorafenib, median age was 65.3 years (range 32.2-83.4) and 85% were male. Median duration of sorafenib was 2.6 months. 12 patients (8%) went on to receive second-line treatment. 37 patients (26%) were deemed eligible for second-line systemic therapy. Primary reasons for ineligibility included ECOG ≥2 (58%), and deterioration to Child-Pugh status B (28%). On Cox regression, improved survival was associated with better ECOG and recurrent disease. (Table). Kaplan-Meier analysis demonstrated that eligibility for second-line treatment was associated with improved median overall survival from end of first-line treatment (8.5 vs. 5.1 months; p<0.01). Conclusions: Only a minority of real-world patients with aHCC were eligible for second-line therapies based on second-line trial criteria. Given the high-rate of attrition, improved first-line treatment options are urgently needed. [Table: see text]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".