A multicenter analysis of prognostic factors in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib.
Bibliographic record
Abstract
e14532 Background: Established clinical prognostic systems for advanced HCC were previously developed in the era of conventional chemotherapy. In 2008, the molecularly targeted agent sorafenib became the new standard of care. It is unclear if these existing models provide useful prognostic information for patients receiving sorafenib. Our aims were to: 1) evaluate the current utility of 4 known prognostic systems, including the Okuda, CLIP, BCLC, and French classification systems; and 2) identify new prognostic factors for patients treated with sorafenib. Methods: All patients diagnosed with advanced HCC from 2008 to 2010 and treated with sorafenib at any 1 of 6 regional cancer cancers in either British Columbia or Ontario, Canada were reviewed. Patients were risk-stratified using each of the 4 established prognostic systems to determine if these correlated with overall survival (OS). Cox proportional hazards models were also constructed to examine for associations between other clinical factors and OS. Results: Of 205 patients identified, 193 were evaluable: median age was 66 years, 79% were men, and 49% / 19 % / 16% had hepatitis B, C, and alcohol-related liver disease, respectively. The mean number of sorafenib cycles was 5.5 and median OS was 7.1 months. Of the 4 known prognostics models, only the French system proved useful where the high, intermediate, and low risk groups demonstrated a median OS of 2.4, 7.0, and 16.8 months, respectively (p<0.0001). Among other clinical factors, univariate analyses showed that poor performance status, presence of ascites, and large tumor size >5cm were associated with worse OS as were an elevated serum AFP, AST, GGT, or ALP level and a low albumin or hemoglobin level (all p<0.05). In multivariate analyses, none of these clinical factors continued to be independently predictive of outcome (all p>0.05). Conclusions: Except for the French classification system, clinical prognostic factors that were identified in the era of conventional chemotherapy were not useful in this Western cohort of advanced HCC patients treated with sorafenib. There is a need for molecular biomarkers that may 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 | 0.000 |
| Bibliometrics | 0.001 | 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".