Robotic liver surgery: literature review and current evidence
Bibliographic record
Abstract
In the field of minimally invasive surgery, robotic surgery (RS) was introduced to overcome drawbacks in laparoscopic surgery. However, its clinical application in hepatobiliary surgery is not yet standardized. This review analyzed the results of RS to clarify the benefits of robotic liver surgery in comparison with standard laparoscopy. Among 112 publications found in the literature, the 72 most relevant were selected and the following data were extracted: patients characteristics, operative procedures, histopathology, short-term and long-term outcomes, and costs. Twenty-nine articles on robotic liver resections, published in the last five years (2015-2020) and including 1831 patients, were analyzed. Twenty-five comparative studies between robotic and laparoscopic surgery were evaluated to underline the differences in operative outcomes. Eventually, 4 sub-group analyses were conducted on hepatocellular carcinoma, gallbladder cancer, hilar cholangiocarcinoma, and colorectal liver metastases. Almost all the authors reported data on safety, feasibility and oncologic effectiveness of RS reaching comparable results with laparoscopy. However, even if robotic surgery showed longer operative time and higher costs, in selected cases it allowed to increase the rate of minimally invasive approach when compared with laparoscopy. Thus, both open and minimally invasive surgery should be provided in a modern hepatobiliary center, including the robotic approach particularly to complex cases, otherwise very demanding by laparoscopy. In conclusion, different techniques should be tailored to each patient, choosing the minimally invasive approach when possible, enhancing patients’ recovery after surgery, especially in cirrhotic livers and in the context of liver transplantation. Although many centers experienced robotic liver surgery, more and larger studies are necessary to define its real benefits relative to laparoscopy, in order to standardize patient selection criteria and techniques.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".