Relationship of ethnicity and overall survival in patients treated with sorafenib for advanced hepatocellular carcinoma.
Bibliographic record
Abstract
BACKGROUND: Although both the SHARP and the Asian-Pacific trials showed improved overall survival (OS) for sorafenib, the magnitude of benefit was substantially less for Asians, who have a higher prevalence of hepatitis B viral (HBV) infection. Whether the worse prognosis is related to ethnicity or to the etiology of hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to identify prognostic factors among patients with HCC who received sorafenib in British Columbia (BC), which has a sizeable Asian population. METHODS: A total of 255 consecutive patients with advanced HCC who initiated sorafenib from January 2008 to February 2013 were identified using our pharmacy database. Clinicopathological variables and outcomes were retrospectively collected. Prognostic factors were assessed by univariate and multivariate analyses. RESULTS: Median age was 63 years, 80.2% were men, and 38% were Asian. Among them, 34.5% had HBV and 29.8% had hepatitis C viral (HCV) infection. In addition, 68.6% had cirrhosis and 45.9% had liver-limited disease. Median progression-free and OS were 3.7 [95% confidence interval (CI): 3.3-4.2] and 7.5 months (95% CI: 5.7-9.2), respectively. On multivariate analysis, Eastern Cooperative Oncology Group performance status (ECOG PS) and HCV positivity correlated with better OS (P<0.001 and 0.04, respectively), but ethnicity did not (P=0.622). CONCLUSIONS: When treated with sorafenib at the same institution, Asians and Caucasians with advanced HCC had similar OS. ECOG PS and HCV were the only significant prognostic factors. A higher proportion of HCV positivity might explain why the SHARP trial achieved better OS when compared to the Asian-Pacific trial.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".