Comparison of safety and effectiveness between robotic and laparoscopic major hepatectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Robotic platform has been increasingly applied in major hepatectomy. However, the role or advantage of robotic approach comparing with laparoscopic approach in major hepatectomy remains controversial. This meta-analysis compares perioperative outcomes of robotic major hepatectomy (RMH) to laparoscopic major hepatectomy (LMH) for hepatic neoplasms. METHODS: PubMed, Web of Science, EMBASE, and Cochrane Library were searched to identify comparative studies compared RMH versus LMH for hepatic neoplasms. The search timeframe was set before May 2023. Main outcomes were mortality, overall morbidities, serious complications, and conversion to open surgery. Secondary outcomes were operative time, intraoperative blood loss, blood transfusion, postoperative length of hospital stay, R0 resection, reoperation, and readmission. Studies were evaluated for quality by Cochrane risk of bias tool or Newcastle-Ottawa scale. Data were pooled as odds ratio (OR) or mean difference (MD). This study was registered at PROSPERO (CRD42023410951). RESULTS: Twelve retrospective cohort studies concerning total 1657 patients (796 RMH, 861 LMH) were included. Meta-analyses showed no significant differences in mortality (OR=1.23, 95% CI=0.50-2.98, P =0.65), overall postoperative complications (OR=0.83, 95% CI=0.65-1.06, P =0.14), operative time (MD=6.47, 95% CI=-14.72 to 27.65, P =0.55), blood transfusion (OR=0.77, 95% CI=0.55-1.08, P =0.13), R0 resection (OR=1.45, 95% CI=0.91-2.31, P =0.12), reoperation (OR=0.76, 95% CI=0.31-1.88, P =0.56), and readmission (OR=0.63, 95% CI=0.28-1.44, P =0.27) between RMH and LMH. Incidence of serious complications (OR=0.60, 95% CI=0.40-0.90, P =0.01), conversion to open surgery (OR=0.41, 95% CI=0.27-0.63, P <0.0001), blood loss (MD=-91.42, 95% CI=-142.18 to -40.66, P =0.0004), and postoperative hospital stay (MD=-0.64, 95% CI=-0.78 to -0.49, P <0.00001) were reduced for RMH versus LMH. CONCLUSIONS: RMH is associated with comparable short-term surgical outcomes and oncologic adequacy compared to LMH when performed by experienced surgeons at large centres. RMH may result in reduced major morbidities, conversion rate, blood loss, and hospital stay, but these results were volatile. Further randomized studies should address the potential advantages of RMH over LMH.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.005 | 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".