Endoscopic ablation versus nephroureterectomy in localized low-grade upper tract urothelial carcinoma: a comparison in terms of cancer-specific and other-cause mortality
Bibliographic record
Abstract
PURPOSE: Guidelines recommend endoscopic ablation in select upper urinary tract urothelial carcinoma (UTUC) patients. To test for differences in cancer-specific mortality (CSM) and other-cause mortality (OCM) in localized non-invasive low-grade UTUC with tumor size < 2 cm treated with endoscopic ablation vs. radical nephroureterectomy. METHODS: Within Surveillance, Epidemiology, and End Results database (2000-2020), we identified UTUC patients treated with either endoscopic ablation or radical nephroureterectomy. After propensity score matching (ratio 1:1), cumulative incidence plots, and competing risks regression models addressed CSM and OCM. RESULTS: Of 249 included UTUC patients, 66 (27%) were treated with endoscopic ablation vs. 183 (73%) with radical nephroureterectomy. Over the study period, endoscopic ablation use increased from 10 to 45% (p = 0.01). After 1:1 propensity score matching, 66 of 66 (100%) endoscopic ablation and 66 of 183 (36%) radical nephroureterectomy patients were included. Ten-year CSM rates were 15.7% after endoscopic ablation vs. 13.9% after radical nephroureterectomy (p = 0.9). Ten-year OCM rates were 46.3% after endoscopic ablation vs. 57.9% after radical nephroureterectomy (p = 0.5). In multivariable competing risks regression models, CSM (hazard ratio 1.10; p = 0.9) and OCM (hazard ratio 0.83; p = 0.5) did not differ according to use of endoscopic ablation vs. radical nephroureterectomy. CONCLUSION: Endoscopic ablation of localized non-invasive low-grade UTUC with tumor size < 2 cm results in absence of cancer-control outcome differences relative to radical nephroureterectomy. This observation validates the current guideline recommendations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".