TAC score better predicts survival than the BCLC following resection of hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND: Heterogeneity in hepatocellular carcinoma (HCC) still exists within the Barcelona clinic liver cancer (BCLC) subcategories. We developed a simple model to better discriminate and predict prognosis following resection. METHODS: Patients who underwent curative-intent resection for HCC were identified from a multi-institutional database. Predictive factors of survival were identified to develop TAC (tumor burden score [TBS], alpha-fetoprotein [AFP], Child-Pugh CP]) score. RESULTS: Among 1435 patients, median TBS was 5.1 (interquartile range [IQR]: 3.2-8.1), median AFP was 18.3 ng/ml (IQR 4.0-362.5), and 1391 (96.9%) patients were classified as CP-A. Factors associated with overall survival (OS) included TBS (low: referent; medium: HR 2.26, 95% CI: 1.73-2.96; high: HR = 3.35, 95% CI: 2.22-5.07), AFP (<400 ng/ml: referent; >400 ng/ml: HR = 1.56, 95% CI: 1.27-1.92), and CP (A: referent; B: HR = 1.81, 95% CI: 1.12-2.92) (all p < 0.05). A simplified risk score demonstrated superior concordance index, Akaike information criteria, homogeneity, and area under the curve versus BCLC (0.620 vs. 0.541; 5484.655 vs. 5536.454; 60.099 vs. 16.194; 0.62 vs. 0.55, respectively), and further stratified patients within BCLC groups relative to OS (BCLC 0, very low: 86.8%, low: 47.8%) (BCLC A, very low: 79.7%, low: 68.1%, medium: 52.5%, high: 35.6%) (BCLC B, low: 59.8%, medium: 43.7%, high: N/A). CONCLUSION: TAC is a simple, holistic score that consistently outperformed BCLC relative to discrimination power and prognostication following resection of HCC.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".