Effect of statin use on prognostic outcomes in hepatocellular carcinoma following liver surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) recurrence remains a significant clinical challenge, even among patients who undergo surgical treatment. Although statins exhibit anticancer properties through several biologically plausible mechanisms, robust clinical evidence supporting their role in preventing HCC recurrence is still limited. This meta-analysis aimed to evaluate the impact of statin use on the prognostic outcomes of patients undergoing either liver transplantation or surgical resection. This study adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines and identified relevant studies from electronic databases, including PubMed , Cochrane Library , Scopus , EBSCOhost , and ProQuest . The quality of the included studies was appraised using the Newcastle-Ottawa Scale tool. A meta-analysis was performed by estimating the hazard ratio (HR) with a 95% confidence interval (CI). A total of 15 studies encompassing 37 160 patients were included, with most evaluating statin use after surgical resection. The overall quality assessment yielded a low risk of bias. Our findings highlight a significant benefit of statin use following either liver transplant or resection, showing a significant improvement in overall survival (HR, 0.50; 95% CI, 0.40-0.61; P < 0.001). Moreover, further analysis also revealed that statins were associated with improved recurrence-free survival of HCC (HR, 0.56; 95% CI, 0.49-0.65; P < 0.001). Our study suggests that statins exert a protective effect, reflected in improved survival and reduced HCC recurrence. These findings support the potential role of statins as an adjunctive therapy in HCC management, potentially improving long-term outcomes. Further research is needed to confirm survival outcomes and safety. p.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".