Quality indicators for the management of high-risk upper tract urothelial carcinoma requiring radical nephroureterectomy
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this article was to identify quality indicators for an optimized management of high-risk upper tract urothelial carcinoma (UTUC) requiring radical nephroureterectomy (RNU). RECENT FINDINGS: RNU with bladder cuff resection is the standard treatment of high-risk UTUC. For the bladder cuff resection, two main approaches are accepted: transvesical and extravesical. Lymph node dissection following a dedicated template should be performed in all high-risk patients undergoing RNU as it improves tumour staging and possibly survival. Postoperative bladder instillation of single-dose chemotherapy should be administered after RNU to decrease the risk of intravesical tumour recurrence. Perioperative systemic chemotherapy should always be considered for advanced cancers. Although level-1 evidence is available for adjuvant platinum-based chemotherapy, neoadjuvant regimens are still being evaluated. SUMMARY: Optimal management of high-risk UTUC requires evidence-based reproducible quality indicators in order to allow guidance and frameworks for clinical practices. Adherence to quality indicators allows for the measurement and comparison of outcomes that are likely to improve prognosis. Based on the literature, we found four evidence-based accepted quality indicators that are easily implementable to improve the management of high-risk UTUC patients treated with RNU: adequate management of the distal ureter/ bladder cuff, template-based lymph node dissection, single-shot postoperative intravesical chemotherapy, and perioperative systemic treatment.
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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 | 0.001 |
| 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 | 0.001 |
| 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".