A New Treatment-integrated Prognostic Nomogram of the Barcelona Clinic Liver Cancer System for Hepatocellular Carcinoma
Bibliographic record
Abstract
Abstract The nomogram of the Barcelona Clinic Liver Cancer (BCLC) has accurate outcome prediction. This study aims to propose a treatment-integrated nomogram derived from BCLC for patients with hepatocellular carcinoma (HCC). A total of 3,371 patients were randomly grouped into derivation (n = 2,247) and validation (n = 1,124) sets. Multivariate Cox proportional hazards model was used to generate the nomogram from tumor burden, cirrhosis, performance status (PS) and primary anti-cancer treatments. Concordance indices and calibration plots were used to evaluate the performance of nomogram. The derivation and validation sets had the same concordance index of 0.774 (95% confidence intervals: 0.717–0.826 and 0.656–0.874, respectively). In calibration plots, survival distributions predicted by the nomogram and observed by the Kaplan-Meier method were similar at 3- and 5-year for patients from derivation and validation sets. Validation group patients divided into 10 subgroups by the original and new treatment-integrated BCLC nomogram were used to evaluate the prognostic performance of integrating primary anti-cancer treatments. Compared to the nomogram of original BCLC system, the treatment-integrated nomogram of BCLC system had larger linear trend and likelihood ratio X2. In conclusion, based on the results of concordance index tests, integrating primary anti-cancer treatments into the BCLC system provides similar discriminatory ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".