Extended lymphadenectomy and adjuvant chemotherapy in muscle-invasive bladder cancer treated by radical cystectomy.
Bibliographic record
Abstract
287 Background: Level 1 evidence is weak for adjuvant chemotherapy (AC) after cystectomy, but surveys indicate physicians refer patients for AC more frequently than for neoadjuvant chemotherapy (NC). The exact benefit of an extended pelvic lymph node dissection (ePLND) remains debated. We addressed the issue of AC and ePLND analyzing two academic centers RC databases with opposite approaches, one using ePLND and AC, the other performing a limited lymph node dissection and no AC. Methods: Two ethics approved RC databases including consecutive BC patients undergoing RC at the University Health Network, Canada and the University of Turku, Finland were studied. Excluding non-urothelial cases and patients receiving NC, 563 patients were available for analysis. Clinicopathological variables, rate and extent of PLND and rate of adjuvant cisplatin-based chemotherapy were analyzed using the χ2-test. Kaplan-Meier method and multivariate Cox regression analysis were used to analyze survival. Results: In Toronto, patients had more extensive PLNDs (>10 nodes removed, 58% vs. 8%, p<0.001), higher rate of nodal metastases (26% vs. 7%, p<0.001), and received more often AC (21% vs. 1%, p<0.001). Positive margin rates were similar (4% in both centers). No BC specific survival difference was demonstrated in ≤ pT2a or in pT4a tumors. There was a trend for improved survival in pT2b tumors (10y BC specific survival 65% vs. 42%, p=0.23) and a significant difference favouring the Toronto cohort in pT3a and pT3b tumors (55% vs. 31%, p=0.025; 43% vs. 28% p=0.06, respectively). In multivariate analysis, N-stage (HR 2.5, 95% CI 1.5-4.1; p<0001) and ePLND (HR 0.53, 95% CI 0.31-0.93, p=0.026) significantly affected disease specific survival. The benefit of AC did not reach significance (HR 0.61, 95% CI 0.36-1.05, p=0.072). An interaction model combining ePLND and AC was significantly related to improved outcome (HR 0.49, 95% CI 0.26-0.92, p=0.026). Conclusions: Despite not being randomized, using 2 study cohorts that received completely opposite managements in terms of ePLND and AC, our results support that ePLND and AC may offer a survival advantage in T2b and especially in T3 BC treated with RC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".