Comparative Analysis of Subclassification Systems in Patients with Intermediate-Stage Hepatocellular Carcinoma (Barcelona Clinic Liver Classification B) Receiving Systemic Therapy
Bibliographic record
Abstract
BACKGROUND: Intermediate-stage hepatocellular carcinoma (BCLC B HCC) occurs in a heterogeneous group of patients and can be addressed with a wide spectrum of treatments. Consequently, survival significantly varies among patients. In recent years, several subclassification systems have been proposed to stratify patients' prognosis. We analyzed and compared these systems (Bolondi, Yamakado, Kinki, Wang, Lee, and Kim criteria) in patients undergoing systemic therapy. METHODS: We considered 171 patients with BCLC B HCC treated with sorafenib as first-line systemic therapy in six Italian centers from 2010 to 2021 and retrospectively applied the criteria of six different subclassification systems. RESULTS: Except for the Yamakado criteria, all the subclassification systems showed a statistically significant correlation to overall survival (OS). In the postestimation analysis, the Bolondi criteria (OS of subgroups 22.5, 11.9, and 6.6 mo, respectively; C-index 0.586; AIC 1338; BIC 1344) and the Wang criteria (OS of subgroups 20.6, 11.9, and 7.0, respectively; C-index 0.607; AIC 1337; BIC 1344) presented the best accuracy. Further analyses of these two subclassification systems implemented with the prognostic factor of alpha-fetoprotein (AFP) > 400 ng/mL have shown an increase in accuracy for both systems (C-index 0.599 and 0.624, respectively). CONCLUSIONS: Intermediate-stage subclassification systems maintain their predictive value also in the setting of systemic therapy. The Bolondi and Wang criteria showed the highest accuracy. AFP > 400 ng/mL enhances the performance of these systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".