Impact of Tumor Burden Score on Conditional Survival after Curative‐Intent Resection for Hepatocellular Carcinoma: A Multi‐Institutional Analysis
Bibliographic record
Abstract
Abstract Background The impact of tumor burden score (TBS) on conditional survival (CS) among patients undergoing curative‐intent resection of hepatocellular carcinoma (HCC) has not been examined to date. Methods Patients who underwent liver resection of HCC between 2000 and 2017 were identified from a multi‐institutional database. The impact of TBS and other clinicopathologic factors on 3‐year conditional survival (CS3) was examined. Results Among 1,040 patients, 263 (25.3%) patients had low TBS, 668 (64.2%) had medium TBS and 109 (10.5%) had high TBS. TBS was strongly associated with OS; 5‐year OS was 39.0% among patients with high TBS compared with 61.1% and 79.4% among patients with medium and low TBS, respectively (p < 0.001). While actuarial survival decreased as time elapsed from resection, CS increased over time irrespective of TBS. The largest differences between 3‐year actuarial survival and CS3 were noted among patients with high TBS (5‐years postoperatively; CS3: 78.7% vs. 3‐year actuarial survival: 30.7%). The effect of adverse clinicopathologic factors including high TBS, poor/undifferentiated tumor grade, microvascular invasion, liver capsule involvement, and positive margins on prognosis decreased over time. Conclusions CS rates among patients who underwent resection for HCC increased as patients survived additional years, irrespective of TBS. CS estimates can be used to provide important dynamic information relative to the changing survival probability after resection of HCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".