Survival Benefit of Experience of Liver Resection for Advanced Recurrent Hepatocellular Carcinoma Treated with Sorafenib: A Propensity Score Matching Analysis
Bibliographic record
Abstract
Several studies have shown that liver resection (LR) confers better survival outcomes in intermediate- and advanced-stage hepatocellular carcinoma (HCC) patients. However, the postoperative recurrence rate is high, and little is known about the survival benefits of LR for recurrent HCC patients who have already received systemic treatment. This study aimed to evaluate the impact of LR on recurrent advanced-stage HCC patients who received sorafenib as a systemic treatment. In this study, 147 advanced HCC patients were enrolled between 1 January 2012 and 31 December 2019. Two study groups were classified, based on whether they underwent LR or not. To reduce the possible selection bias, a propensity score matching (PSM) analysis was performed. The primary study endpoint was set as overall survival (OS), and the secondary endpoint was set as progression-free survival (PFS). Our study results revealed that advanced HCC patients who received sorafenib with LR had a longer OS than did those without LR, whether before or after PSM (15.0 months vs. 6.0 months, HR 0.45, 95% CI 0.31–0.67, p < 0.001; 15.0 months vs. 5.0 months, HR 0.46, 95% CI 0.28–0.76, p = 0.001). Similar results were obtained in PFS, before or after PSM (4.14 months vs. 2.60 months, HR 0.60, 95% CI 0.40–0.89, p = 0.01; 4.57 months vs. 2.63 months, HR 0.58, 95% CI 0.34–0.97, p = 0.037). Multivariate analysis showed that the experience of LR was independent of other factors associated with better OS and PFS, whether before or after PSM (p < 0.05). Therefore, advanced HCC patients who have undergone liver resection should be encouraged to continue sorafenib treatment to improve prognosis.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".