Robotic-assisted partial nephrectomy using the HugoTM robotic-assisted surgery platform
Bibliographic record
Abstract
INTRODUCTION: ) is one of the newest platforms on the market and has little surgical outcomes data. Herein, we describe our early experience performing robotic-assisted partial nephrectomy (RAPNx) with the Hugo™ RAS platform. METHODS: We conducted a retrospective review of patients who underwent a RAPNx with the Hugo™ RAS platform between April and December 2023 at the University Health Network in Toronto, ON. One surgeon performed all procedures using a three-arm transperitoneal approach. Anesthetic, operative, and pathologic reports were assessed to collect pre-, intra- and postoperative variables. RESULTS: Eleven patients were included. The mean age was 51 years, 45.0% were female, and 63.6% had a right-sided mass. Mean tumor size was 2.9 cm. Mean warm ischemia time was 18.9 minutes (standard deviation [SD] 7.12) and mean estimated blood loss was 179 ml (SD 63.6). Mean robot docking time was 232 seconds (SD 106.5), mean total console time was 93 minutes (SD 21.4), and mean total operative time was 165.6 minutes (SD 34.1). There were no intraoperative complications. On pathology review, most tumors were a clear-cell variant (72.7%) and staged pT1a (81.8%). All margins were negative. One patient sustained a port site infection. CONCLUSIONS: This is the first North American case series using the Hugo™ RAS platform for RAPNx. Our findings underscore that the platform is safe and effective for performing RAPNx with comparable outcomes to other robotic platforms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".