Comparison of Perioperative Outcomes Between Open and Robotic Radical Cystectomy: A Population-Based Analysis
Bibliographic record
Abstract
INTRODUCTION: Radical cystectomy represents the standard of care for muscle-invasive bladder cancer (MIBC). Due to its novelty the use of robotic radical cystectomy (RARC) is still under debate. We examined intraoperative and postoperative morbidity and mortality in addition to impact on length of stay (LOS) and total hospital charges (THCGs) of RARC compared with open radical cystectomy (ORC). MATERIALS AND METHODS: Within National Inpatient Sample (2008-2013), we identified patients with nonmetastatic bladder cancer treated with either ORC or RARC. We relied on inverse probability of treatment weighting to reduce the effect of inherent differences between ORC vs RARC. Multivariable logistic regression (MLR) and multivariable Poisson regression (MPR) models were used. RESULTS: Of all 10,027 patients, 12.6% underwent RARC. Between 2008 and 2013, RARC rates increased from 0.8% to 20.4% [estimated annual percentage change (EAPC): +26.5%, 95% confidence interval (CI): +11.1 to +48.3; p = 0.035] and RARC THCGs decreased from 45,981 to 31,749 United States dollars (EAPC: -6.8%, 95% CI: -9.6 to -3.9; p = 0.01). In MLR models RARC resulted in lower rates of overall complications [odds ratio (OR): 0.6; p < 0.001] and transfusions (OR: 0.44; p < 0.001). In MPR models, RARC was associated with shorter LOS (relative risk 0.91; p < 0.001). Finally, higher THCGs (OR: 1.09; p < 0.001) were recorded for RARC. Data are retrospective and no tumor characteristics were available. CONCLUSION: RARC is related to lower rates of overall complications and transfusions rates. In consequence, RARC is a safe and feasible technique in select MIBC patients. Moreover, RARC is associated with shorter LOS, although higher THCGs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".