Robot‐Assisted Versus Open Radical Cystectomy: Comparison of Adverse In‐Hospital Outcomes
Bibliographic record
Abstract
PURPOSE: To quantify improvements in adverse in-hospital outcomes between historical and contemporary robot-assisted radical cystectomy (RARC) versus historical and contemporary open RC (ORC). MATERIAL AND METHODS: Within the National Inpatient Sample (2010-2019), RARC and ORC ileal conduit diversion patients were identified. Multivariable logistic and Poisson regression models were fitted. RESULTS: Of RARC patients, 1343 (39%) were historical (2010-2014) and 2087 (61%) were contemporary (2015-2019). Of ORC patients, 5812 (54%) were historical and 5019 (46%) were contemporary. Versus historical counterparts, contemporary RARC patients exhibited significantly better adverse in-hospital outcomes in 9 of 13 categories, with improvements ranging from -82% for intraoperative complications to -22% for cumulative postoperative complications. Similarly, versus historical, contemporary ORC patients also exhibited significantly better adverse in-hospital outcomes in 9 of 13 categories, with improvements ranging from -72% for intraoperative complications to -12% for median length of stay (LOS). When contemporary RARC was compared to contemporary ORC, RARC adverse in-hospital outcomes were better in 7 of 13 comparisons, with improvements ranging from -55% for blood transfusions to -18% for median LOS. Similarly, when historical RARC was compared to historical ORC, RARC adverse in-hospital outcomes were better in 6 of 13 comparisons, with improvements ranging from -55% for blood transfusions to -15% for median LOS. CONCLUSION: The magnitude of the improvement in adverse in-hospital outcomes was comparable between contemporary versus historical RARC (nine improved categories) and contemporary versus historical ORC (nine improved categories). However, contemporary RARC outperformed contemporary ORC in 7 of 13 categories of adverse in-hospital outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".