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]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".