Outcomes of robotic‐assisted versus open radical cystectomy in a large‐scale, contemporary cohort of bladder cancer patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To test for differences in perioperative outcomes and total hospital costs (THC) in nonmetastatic bladder cancer patients undergoing open (ORC) versus robotic-assisted radical cystectomy (RARC). METHODS: We relied on the National Inpatient Sample database (2016-2019). Statistics consisted of trend analyses, multivariable logistic, Poisson, and linear regression models. RESULTS: Of 5280 patients, 1876 (36%) versus 3200 (60%) underwent RARC versus ORC. RARC increased from 32% to 41% (estimated annual percentage change [EAPC]: + 8.6%; p = 0.02). Rates of transfusion (8% vs. 16%), intraoperative (2% vs. 3%), wound (6% vs. 10%), and pulmonary (6% vs. 10%) complications were lower in RARC patients (all p < 0.05). Moreover, median length of stay (LOS) was shorter in RARC (6 vs. 7days; p < 0.001). Conversely, median THC (31,486 vs. 27,162$; p < 0.001) were higher in RARC. Multivariable logistic regression-derived odds ratios addressing transfusion (0.49), intraoperative (0.53), wound (0.68), and pulmonary (0.71) complications favored RARC (all p < 0.01). In multivariable Poisson and linear regression models, RARC was associated with shorter LOS (Rate ratio:0.86; p < 0.001), yet higher THC (Coef.:5,859$; p < 0.001). RARC in-hospital mortality was lower (1% vs. 2%; p = 0.04). CONCLUSIONS: RARC complications, LOS, and mortality appear more favorable than ORC, but result in higher THC. The favorable RARC profile contributes to its increasing popularity throughout the United States.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".