Lymph node dissection for upper tract urothelial carcinoma: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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 it