Retzius-Sparing Robot-Assisted Radical Prostatectomy Using the Hinotori Surgical Robot System Platform: Report of the First Series of Experiences
Bibliographic record
Abstract
Background: The aim of this study is to describe the first series of six patients undergoing Retzius-sparing robot-assisted radical prostatectomy (rs-RARP) using the hinotori surgical robot system (hinotori SRS) and to compare the treatment outcomes with those achieved with the da Vinci surgical platform. Methods: This study included 20 cases involving the rs-RARP procedure (hinotori: N = 6; da Vinci: N = 14) that were performed between May 2021 and April 2024 in a single institution. Results: No significant differences were observed between the hinotori and da Vinci groups regarding the preoperative findings. In the hinotori group, there were four cases of pT2 that showed negative surgical margins in all the cases. However, positive surgical margins were observed in two of the cases with pT3. The surgical outcomes were also similar between the two groups except for console time, which tended to be shorter in the da Vinci group (p = 0.058). There were no major complications in the initial six cases with the hinotori SRS. Immediate urinary continence was observed in 50% of the cases with the hinotori group compared with 64% for the da Vinci group. Conclusion: This is the first study to report cases of rs-RARP performed on a hinotori SRS. It seems that the hinotori SRS shows similar treatment outcomes compared with the cases treated via the da Vinci platform.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".