Robot-assisted versus laparoscopic nephroureterectomy for uppertract urothelial cancer: A population-based assessment of costs and perioperative outcomes
Bibliographic record
Abstract
INTRODUCTION: We compared short-term outcomes and costs between robotic-assisted nephroureterectomy (RANU) and laparoscopic radical nephroureterectomy (LNU) in a large population-based cohort of patients with upper-tract urothelial carcinoma (UTUC). METHODS: Overall, 1914 patients with UTUC treated with RANU or LNU between 2008 and 2010 within the Nationwide Inpatient Sample were abstracted. Propensity-score matching was performed to account for inherent differences between patients undergoing RANU and LNU. Multivariable logistic regression models were fitted to compare postoperative complications, blood transfusions, prolonged length of stay, and costs between the 2 procedures. RESULTS: Overall, a weighted estimate of 1199 (62.6%) and 715 (37.4%) patients received LNU and RANU, respectively. In multivariable analyses no significant differences were observed in postoperative transfusion and length of stay between the 2 surgical approaches (all p > 0.1). However, patients undergoing RANU were less likely to experience any complications compared to their counterparts undergoing LNU (p = 0.04). The utilization of RANU was associated with substantially higher costs compared to the laparoscopic approach. Our study is limited by its retrospective nature and the lack of adjustment for tumour stage and grade. CONCLUSIONS: Our results support the safety and feasibility of RANU for the treatment of UTUC. Indeed, the use of the robotic approach was associated with lower probability of experiencing perioperative complications compared to LNU. On the other hand, the utilization of RANU is associated with higher costs compared to LNU.
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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) | 0.000 | 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".