Open, laparoscopic, and robotic radical nephroureterectomy for upper tract urothelial carcinoma
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to compare surgical outcomes and costs between robotic radical nephroureterectomy (RNU), laparoscopic radical nephroureterectomy (LNU), and open radical nephroureterectomy (ONU), and to assess the relevance of the tetrafecta as a composite outcome on survival parameters after nephroureterectomy (NU). METHODS: Operative and oncologic followup data was retrospectively collected on patients who underwent NU from 2006-2022 at our institution. The tetrafecta was defined as a true bladder cuff, lymph node dissection, negative surgical margins, and no postoperative complications. Cox proportional hazards regression was used to assess the impact of surgical approach on survival outcomes. RESULTS: A total of 248 patients were included in the analysis (145 RNU, 61 LNU, and 42 ONU). The complication rate differed by approach and was lowest in RNU (p<0.01). Cancer-specific survival (CSS) differed between ONU and RNU patients, with ONU patients 2.51 times as likely to die from their cancer. Retroperitoneal recurrence-free survival (RPFS) differed between ONU and RNU patients, with ONU patients 7.22 times more likely to experience a retroperitoneal recurrence (p=0.0013). Variable surgical costs were lower in LNU compared to ONU (p=0.028) and direct inpatient hospital costs were lowest with RNU (p<0.01). Eighty-one patients met criteria for the tetrafecta. RNU patients were more likely to achieve the tetrafecta compared to LNU (p<0.01) and ONU (p<0.01) patients. No differences in survival parameters existed between patients who did and did not achieve the tetrafecta. CONCLUSIONS: Most oncologic outcomes after NU do not differ by approach on long-term followup; however, CSS and RPFS appear to differ between RNU and ONU. ONU has traditionally been considered the approach with the lowest cost; however, our analysis demonstrates both RNU and LNU require lower costs than ONU, depending on the cost parameter analyzed. Among all approaches, the tetrafecta is best achieved with RNU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".