Open Versus Robotic Cystectomy: A Propensity Score Matched Analysis Comparing Survival Outcomes
Bibliographic record
Abstract
Background: To assess the differential effect of robotic assisted radical cystectomy (RARC) versus open radical cystectomy (ORC) on survival outcomes in matched analyses performed on a large multicentric cohort. Methods: The study included 9757 patients with urothelial bladder cancer (BCa) treated in a consecutive manner at each of 25 institutions. All patients underwent radical cystectomy with bilateral pelvic lymphadenectomy. To adjust for potential selection bias, propensity score matching 2:1 was performed with two ORC patients matched to one RARC patient. The propensity-matched cohort included 1374 patients. Multivariable competing risk analyses accounting for death of other causes, tested association of surgical technique with recurrence and cancer specific mortality (CSM), before and after propensity score matching. Results: Overall, 767 (7.8%) patients underwent RARC and 8990 (92.2%) ORC. The median follow-up before and after propensity matching was 81 and 102 months, respectively. In the overall population, the 3-year recurrence rates and CSM were 37% vs. 26% and 34% vs. 24% for ORC vs. RARC (all p values > 0.1), respectively. On multivariable Cox regression analyses, RARC and ORC had similar recurrence and CSM rates before and after matching (all p values > 0.1). Conclusions: Patients treated with RARC and ORC have similar survival outcomes. This data is helpful in consulting patients until long term survival outcomes of level one evidence is available.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".