Efficacy and Safety of the “Trisection Method” Training System for Robot-Assisted Radical Cystectomy at a Single Institution in Japan
Bibliographic record
Abstract
To maintain a surgeon's concentration, reduce fatigue, and train young surgeons, surgical procedures for bladder cancer are divided into the following parts: robot-assisted radical cystectomy (RARC), bowel reconstruction, and totally intracorporeal urinary diversion (ICUD) (RARC+ICUD). Each part is performed by a different surgeon (Trisection method). We retrospectively evaluated the efficacy and safety of this approach at a single institution in Japan. One hundred consecutive patients who underwent RARC+ICUD at Gifu University Hospital between November 2018 and August 2022 were included in this study. The patient background, surgical outcomes, and postoperative complications were compared between surgeries by first-, second-, and third-generation surgeons. The overall survival (OS) and recurrence-free survival (RFS) were compared between surgeries by each generation. Of the 100 patients, 19, 38, and 43 RARCs were performed by first-, second-, and third-generation surgeons, respectively. There were 35, 25, and 39 patients who underwent ileal conduit, neobladder, and ureterocutaneostomy, respectively. No significant differences were found among the patients respective to the type of ICUDs. Although the first-generation surgeon had a significantly shorter operative time with RARC, the surgical time for bowel reconstruction, length of hospital stays, and incidence of postoperative complications were not significantly different among the groups. Additionally, OS and RFS did not differ significantly among the generations. The "Trisection method" is an effective and safe concept with no difference in outcomes between the generations of surgeons.
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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 | 0.001 | 0.002 |
| 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".