Comparative real-world survival outcomes of muscle-invasive bladder cancer treated with bladder-only vs. whole-pelvis concurrent chemoradiation
Bibliographic record
Abstract
INTRODUCTION: Elective pelvic nodal irradiation for patients with muscle-invasive bladder cancer (MIBC) undergoing trimodal therapy (TMT ) is controversial. In patients with node-negative (N0) MIBC, the benefit of elective whole-pelvis concurrent chemoradiation (WP-CCR) compared to bladder-only (BO )-CCR has not been demonstrated. Using real-world data from the National Cancer Database (NCDB ), we sought to compare the overall survival (OS ) between BO-CCR and WP-CCR for MIBC. METHODS: Using the 2020 NCDB Participant User File, we identified cases of MIBC diagnosed between 2017 and 2019. We selected patients with clinical T2-T4aN0M0 disease receiving CCR as first-line treatment. CCR was defined as transurethral resection of bladder tumor followed by ≥40 Gy radiation to the bladder with concurrent single- or multiple-agent chemotherapy. Based on elective nodal irradiation status, patients were stratified as having received BO-CCR vs. WP-CCR. OS analysis was performed using summary three-month conditional landmark, inverse probability treatment weighting (IPTW)-adjusted Kaplan-Meier estimates, and Cox regression. RESULTS: A total of 604 patients receiving CCR for MIBC were identified: 367 (60.8%) BO-CCR and 237 (39.2%) WP-CCR. Before IPTW, the groups were imbalanced in terms of baseline characteristics. The median followup of the weighted population was 42.3 months (interquartile range 18.1-49.1 months). In IPTW-adjusted Cox proportional hazards regression analysis, WP-CCR was associated with a significant OS benefit compared to BO-CCR (adjusted hazard ratio 0.72, 95% confidence interval 0.54-0.96, p=0.026). CONCLUSIONS: In the setting of CCR for N0 MIBC, this retrospective NCDB analysis revealed that WP-CCR was associated with a benefit in OS compared to BO-CCR.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".