Lymph node dissection for upper tract urothelial carcinoma: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, investigating the role of lymph node dissection (LND) during nephroureterectomy (NU) for upper tract urothelial carcinoma (UTUC); focussing on survival and complication outcomes. METHODS: A comprehensive systematic search was completed using a combination of Medical Subject Headings terms and keywords related to UTUC and LND on multiple databases. Meta-analyses were performed when outcomes were reported under the same definition in two or more studies. Where meta-analysis was not possible, outcomes were reviewed in a narrative manner. RESULTS: A total of 21 studies were included in the qualitative analysis and 11 cohort studies in the quantitative analysis. Our review did not detect significant improvement in recurrence-free survival (RFS) (hazard ratio [HR] 0.89, 95% confidence interval [CI] 0.41-1.92), cancer-specific survival (CSS) (HR 0.89, 95% CI 0.54-1.46) and overall survival (OS) (HR 1.10, 95% CI 0.93-1.30). However, when focussing on studies only including patients with pT2/pT3 UTUC, not performing LND significantly worsened RFS (HR 2.83, 95% CI 1.72-4.66). Reports of removing more than eight lymph nodes may also provide prognostic benefits in pN0 patients. The performance of LND was not associated with a higher rate of postoperative complications (risk ratio 1.06, 95% CI 1.00-1.13). CONCLUSION: ; CSS: cancer-specific survival; HR: hazard ratio; LND: lymph node dissection; NU: nephroureterectomy; OS: overall survival; PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RFS: recurrence-free survival; RoB, risk of bias; RR: risk ratio; (UT)UC: (upper tract) urothelial carcinoma.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".