Comparison of outcomes in robot-assisted colon cancer surgery using Da Vinci Xi, Hugo™ RAS, and Versius®: The COMPAR-CRC multiplatform study
Bibliographic record
Abstract
Since the introduction of the da Vinci® robotic system, robot-assisted colon resection has gained popularity because of its the potential technical advantages. Recently, two new CE-marked platforms have become available in Europe: Hugo™ RAS and Versius®. We present the first prospective case series comparing these three robotic systems. This exploratory, prospective study enrolled 45 consecutive adult patients undergoing robotic colon resection between February and December 2024, as part of the COMPAR trial. Two experienced colorectal surgeons performed all procedures across two surgical units. Each robotic platform was used in 15 cases. The primary outcomes were conversion to laparoscopy or open surgery and intra-operative complications. The secondary outcomes included post-operative recovery, oncological results, and platform-specific technical parameters. The mean age was 66.8 years and 68.9% of patients underwent surgery for colon cancer. No conversions occurred in the da Vinci group, whereas 2 and 3 conversions to laparoscopy were recorded with Hugo™ RAS and Versius®, respectively. One intra-operative instrument malfunction occurred with Hugo™ RAS, and one surgical complication was reported in each group. No significant differences emerged in post-operative recovery or oncological outcomes. Versius® cases required more frequent use of laparoscopic energy devices ( p < 0.001). Hugo™ RAS was associated with a longer total operating room time ( p = 0.022) and longer incision length ( p = 0.005). Robotic colorectal surgery with all three platforms is feasible when performed by expert surgeons. While early outcomes are encouraging, larger comparative trials are needed to confirm differences in recovery and oncological efficacy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".