The impact of robotics in liver surgery: A worldwide systematic review and short‐term outcomes meta‐analysis on 2,728 cases
Bibliographic record
Abstract
BACKGROUND: The dissemination of robotic liver surgery is slow-paced and must face the obstacle of demonstrating advantages over open and laparoscopic (LLS) approaches. Our objective was to show the current position of robotic liver surgery (RLS) worldwide and to identify if improved short-term outcomes are observed, including secondary meta-analyses for type of resection, etiology, and cost analysis. METHODS: A PRISMA-based systematic review was performed to identify manuscripts comparing RLS vs open or LLS approaches. Quality analysis was performed using the Newcatle-Ottawa score. Statistical analysis was performed after heterogeneity test and fixed- or random-effect models were chosen accordingly. RESULTS: After removing duplications, 2728 RLS cases were identified from the final set of 150 manuscripts. More than 75% of the cases have been performed on malignancies. Meta-analysis from the 38 comparative reports showed that RLS may offer improved short-term outcomes compared to open procedures in most of the variables screened. Compared to LLS, some advantages may be observed in favour of RLS for major resections in terms of operative time, hospital stay and rate of complications. Cost analyses showed an increased cost per procedure of around US$5000. CONCLUSIONS: The advantages of RLS still need to be demonstrated although early results are promising. Advantages vs open approach are demonstrated. Compared to laparoscopic surgery, minor perioperative advantages may be observed for major resections although cost analyses are still unfavorable to the robotic approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".