Impact of Anatomical Resection on Non-transplantable Recurrence Among Patients with Hepatocellular Carcinoma: An International Multicenter Inverse Probability of Treatment Weighting Analysis
Bibliographic record
Abstract
BACKGROUND: Among patients with hepatocellular carcinoma (HCC), the impact of anatomic resection (AR) versus non-anatomic resection (NAR) on non-transplantable recurrence (NTR) remains poorly defined. We sought to compare the risk of NTR among patients treated with AR versus NAR as the primary surgical strategy for HCC. PATIENTS AND METHODS: Patients with HCC within Milan criteria who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. The inverse probability of treatment weighting (IPTW) method was utilized to compare short- and long-term outcomes among patients undergoing AR versus NAR. RESULTS: Among 1038 patients, 747 (72.0%) patients underwent AR, while 291 (28.0%) patients underwent NAR. After IPTW adjustment, patients who underwent AR had better 5-year recurrence-free survival than individuals treated with NAR (63.9 vs. 52.0%; hazard ratio [HR] 0.78; 95% confidence interval [CI] 0.62-0.99); however, there was no difference in 5-year overall survival (80.2 vs. 75.6%; HR 0.76; 95% CI 0.55-1.05). Notably, individuals who underwent AR were less likely to have a NTR versus individuals treated with NAR (3-year NTR 9.8 vs. 14.4%; HR 0.62; 95% CI 0.40-0.96). In particular, AR was associated with a lower risk of NTR among patients with a medium tumor burden score (TBS) (HR 0.53; 95% CI 0.28-0.99), while the benefit among patients with a low TBS was less pronounced (HR 0.73; 95% CI 0.40-1.32). CONCLUSIONS: AR was associated with a lower risk of NTR and improved recurrence-free survival (RFS) among patients with HCC, especially individuals with higher TBS. An anatomically defined surgical approach should be strongly considered in patients with a higher HCC tumor burden.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".