A comparison of prognostic systems in hepatocellular carcinoma treated with sorafenib.
Bibliographic record
Abstract
e17696 Background: Prognostic systems remain important tools in clinical oncology to relay information to patients (pts) and to aid in therapeutic decision-making, but many were developed before the introduction of biologics. For hepatocellular carcinoma (HCC), it is unclear which of the available prognostic systems is most appropriate in an era where sorafenib has become the standard of care in advanced disease. We aimed to 1) evaluate the utility of four different prognostic scores (Okuda, BCLC, CLIP and French) and 2) identify additional independent predictors of overall survival (OS) in the setting of HCC pts treated with sorafenib. Methods: We included 339 pts with HCC treated with sorafenib from 2007 to 2014 across 3 Canadian provinces. Pt demographics, laboratory values, radiology reports, additional treatments received, performance status, and Child-Pugh scores were collected. The four existing prognostic systems were evaluated by Kaplan-Meier curves and c-statistics were ascribed to each. Independent parameters predictive of OS were identified first by univariable analyses and further validated by multivariable analyses. Results: The pt cohort was predominantly male (81%) with representation from both early (T1, 28%) and late (M1, 25%) stages of disease at presentation. Primary etiologies of liver cirrhosis included HBV (38%), HCV (26%) and EtOH (18%). Pts received a median number of three cycles of sorafenib. The median OS from the initiation of sorafenib was 7.3 months. Comparison of the four prognostic systems revealed that the French and CLIP scoring systems performed the strongest (c-indices 0.73 and 0.68, respectively), while BCLC and Okuda performed the worst (c-indices 0.64 and 0.52, respectively). Multivariable analyses confirmed that portal vein invasion, AFP > 400 and WHO performance status > 0 were significantly and independently predictive of OS. Conclusions: The French prognostic score may be better suited for pts receiving sorafenib compared to other prognostic scales, but modern prognostication of HCC may be further enhanced by a novel system that considers all of the predictive variables. This study highlights a need to revisit the utility of prognostic scores developed prior to the era of biologics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".