Impact of Maximal Transurethral Resection on Pathological Outcomes at Cystectomy in a Large, Multi-institutional Cohort
Bibliographic record
Abstract
PURPOSE: While the presence of residual disease at the time of radical cystectomy for bladder cancer is an established prognostic indicator, controversy remains regarding the importance of maximal transurethral resection prior to neoadjuvant chemotherapy. We characterized the influence of maximal transurethral resection on pathological and survival outcomes using a large, multi-institutional cohort. MATERIALS AND METHODS: We identified 785 patients from a multi-institutional cohort undergoing radical cystectomy for muscle-invasive bladder cancer after neoadjuvant chemotherapy. We employed bivariate comparisons and stratified multivariable models to quantify the effect of maximal transurethral resection on pathological findings at cystectomy and survival. RESULTS: < .05, respectively). In multivariable models, maximal transurethral resection was associated with downstaging at cystectomy (adjusted odds ratio 1.6, 95% CI 1.1-2.5). In Cox proportional hazards analysis, maximal transurethral resection was not associated with overall survival (adjusted HR 0.8, 95% CI 0.6-1.1). CONCLUSIONS: In patients undergoing transurethral resection for muscle-invasive bladder cancer prior to neoadjuvant chemotherapy, maximal resection may improve pathological response at cystectomy. However, the ultimate effects on long-term survival and oncologic outcomes warrant further investigation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".