Long-Term Efficacy of Laparoscopic Radiofrequency Ablation in Early Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Objective: The aim of this study was to investigate the long-term efficacy of laparoscopic radiofrequency ablation (LRFA) in early hepatocellular carcinoma (HCC) compared with other surgical procedures. Methods: A literature search of Cochrane library, PubMed, and Embase through October 2018 was conducted by two investigators (J.-F.G. and F.Y.) independently. The quality of included studies was estimated by the Newcastle–Ottawa Scale. Review Manager 5.3 software was used for meta-analysis, and either fixed- or random-effects model was used according to the heterogeneity of included studies. The chi-square test was used for heterogeneity analysis of included studies, and subgroup analysis was conducted to estimate the heterogeneity between each study and also to estimate the efficacy of different studies. Results: A total of 11 studies involving 1691 patients were included in this analysis. Patients undergoing hepatic resection (HR) had higher 3-, 5-year overall survival rate, 3-year disease-free survival rate, and lower local recurrence rate than those undergoing LRFA. However, patients undergoing LRFA had higher 3-, 5-year overall survival rate than those undergoing other minimally invasive ablation, although there was no statistical difference in local recurrence rate or disease-free survival rate. Conclusion: HR is still an ideal choice for early HCC. If minimally invasive ablation is an alternative treatment, LRFA will be better than other minimally invasive options.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".