S2454 Comparative Outcomes of Robotic vs Laparoscopic Liver Resection for Malignancy: An Updated Meta-Analysis
Bibliographic record
Abstract
Introduction: Surgical resection remains the mainstay treatment for liver malignancies. Recent advances in surgical techniques have introduced robotic-assisted liver resection (RLR) as a viable alternative to conventional laparoscopic liver resection (LLR). Both approaches aim to provide minimally invasive options, but the relative advantages in terms of surgical outcomes, recovery times, and oncological efficacy are still debated. This updated meta-analysis aims to systematically compare the outcomes of RLR and LLR for liver malignancies. Methods: A comprehensive search was conducted in PubMed, Embase, Google Scholar, ScienceDirect, PLOS ONE, Cochrane databases, and ClinicalTrials.gov up to November 30, 2024. The quality of the included studies was assessed using the Newcastle–Ottawa quality assessment scale and the Cochrane Risk of Bias (RoB) tool. The mean difference with 95% CI was used for continuous variables; risk ratio with 95% CI was used for dichotomous variables; and hazard ratio with 95% CI was used for survival-related variables. Meta-analysis was performed using a random-effects model. Results: Eight high-quality cohort studies and 1 RCT with 1785 patients were included (624 and 1161 cases for RLR and LLR, respectively). LLR was associated with a significantly shorter hospital stay compared to RLR (RR 0.46, 95% CI: 0.75 to 0.16, ) and a shorter operating time (RR 26.73, 95% CI: 15.90 to 37.57, ). Other outcomes, including conversion to laparotomy, major morbidity, 5-year overall survival and 5-year disease-free survival, showed no significant differences between the 2 techniques. Conclusion: LLR demonstrated shorter hospital stay and reduced operating time compared to RLR, with no significant difference in long-term surgical and oncological outcomes. These findings suggest that LLR may be the preferable approach for patients where shorter recovery and operative efficiency are priorities. Further research is needed to explore long-term surgical and oncological outcomes.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.065 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".