Comparison of prognostic models for hepatocellular carcinoma (HCC) in patients treated with the sorafenib: Results from a Canadian multi-center HCC database.
Bibliographic record
Abstract
e15653 Background: Several staging systems and models (TNM, BCLC, Okuda, CLIP and ALBI) have been developed to estimate the prognosis of patients with HCC. Most of these were developed prior to the prevalent use of sorafenib. The purpose of this study was to compare the prognostic and discriminatory power of these models in predicting survival for HCC patients treated with sorafenib. Methods: Patients who received sorafenib for the treatment of HCC between January 1, 2008 and June 30, 2015 in the provinces of British Columbia and Alberta, as well as Princess Margaret Cancer Centre and Sunnybrook Odette Cancer Centre in Toronto, Ontario were included. Survival outcomes for each model were assessed with Kaplan-Meier (KM) curves and compared with the log-rank test. Time dependent area under the curve (t-AUC) was used to test the discriminatory power of each model (higher t-AUC = more discriminatory power). Akaike information criterion (AIC), a measure of goodness-of fit of models while penalizing overly complex models, was used to compare the models (lower AIC = better model). Results: A total of 681 patients were included in this analysis. Median age was 64 years (range 8-91). Majority were males (80%), had a Child-Pugh score A (86%), ECOG performance status 0 (30%) and 1 (60%). 37% of patients were of East Asian ethnicity. Most common etiology for liver disease was hepatitis B (33%) and C (29%). At start of sorafenib, most patients were BCLC stage C (92%) and TNM stage IV (61%). The median overall survival for the entire cohort was 9.2 months (95% CI 8-10.4). See table below for t-AUC and AIC results. Conclusions: According to ourlarge multi-center study, CLIP appears to be the most informative in predicting survival in HCC patients treated with sorafenib. Prospective studies are needed to determine its role in patient selection for clinical trials and in guiding treatment decisions. The TNM and BCLC staging systems were the least useful in predicting survival in this population. [Table: see text]
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".