A population-based analysis of prognostic factors in patients with advanced hepatocellular carcinoma treated with sorafenib.
Bibliographic record
Abstract
163 Background: Available clinical prognostic scoring systems for advanced hepatocellular carcinoma (HCC) were developed in the era of conventional chemotherapy. In 2008, the molecularly targeted agent sorafenib became the new standard of care for advanced HCC due to its survival benefit. The utility of these prognostic models in the setting of sorafenib is unclear. Our aims were to assess for new prognostic factors in patients treated with sorafenib and compare these with known prognostic systems. Methods: All patients diagnosed with advanced HCC from 2008 to 2010 in British Columbia, Canada and treated with sorafenib at any 1 of 5 regional cancer centers were eligible. Based on the established Okuda, CLIP, Barcelona, and French staging systems, we collected baseline demographic and disease characteristics of patients prior to receipt of sorafenib. Multivariate logistic regression models were constructed to examine for associations between these clinical factors and overall survival. Results: Of 183 patients identified, 152 were evaluable: median age was 63 years, 78% were men, average number of sorafenib treatment was 5.3 cycles, and median overall survival was 9.6 months. The prevalence of hepatitis B, hepatitis C, and alcohol-related liver disease were 32%, 15%, and 11%, respectively. Univariate analyses showed that poor performance status, presence of clinical ascites, as well as elevated serum AST, GGT, ALP, bilirubin and platelet levels were each associated with worse overall survival (all p<0.05). In multivariate analyses, however, none of these clinical factors continued to be independently predictive of outcome (all p>0.05). Conclusions: Traditional clinical prognostic factors developed in the era of conventional chemotherapy do not appear to have the same prognostic utility in this contemporary Western cohort of advanced HCC patients treated with sorafenib. This observation underscores the need to identify molecular biomarkers that provide better prognostic information.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".