Minimizing the learning curve for robotic-assisted radical cystectomy
Bibliographic record
Abstract
INTRODUCTION: Studies published to date have suggested non-inferiority of robotic-assisted radical cystectomy (RARC) compared to open radical cystectomy (ORC), while few centers in Canada have adopted this approach. Though multifactorial, the learning curve and operative time are often discussed barriers. Herein, we present outcomes from the largest Canadian cohort of RARC performed to date. METHODS: We conducted a retrospective chart review of all patients undergoing RARC by a single surgeon with greater than 1500 robot-assisted radical prostatectomy (RARP) experience at our institution from May 2020 to December 2021. Clinicopathological, intraoperative, and postoperative data, as well as complications in the first 90 days, were collected. Regression analysis was used to determine the relationship between case volume and operative time/lymph node yield. RESULTS: A total of 31 patients underwent RARC during the study period, 26 of which were male. The median length of stay was six days (Q1-Q3 5-10), while days alive and out of hospital at 90 days were 83 days (Q1-Q3 80-85). Soft tissue margins were positive in 9.6% (3/31) of patients. Median lymph node yield was 17.0 lymph nodes (Q1-Q3 11-23). Median operative time was 241 minutes (Q1-Q3 228-252) in the ileal conduit group and 320 minutes (Q1-Q3 302-337) in the neobladder group. We observed four Clavien-Dindo grade >3 complications. The 90-day readmission rate and mortality rate were 17.2% (5) and 0% (0), respectively. There was no correlation between case volume and any outcome variables. CONCLUSIONS: Previous high-volume experience performing RARP reduces the learning curve for performing RARC, with similar short-term outcomes to high-volume centers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".