Mortality rates in radical cystectomy patients with bladder cancer after radiation therapy for prostate cancer
Bibliographic record
Abstract
Objective To conduct a population‐based study examining cancer‐specific mortality (CSM) and other‐cause mortality (OCM) differences in patients with radiation‐induced secondary bladder cancer (RT‐BCa) vs those with primary bladder cancer (pBCa) undergoing radical cystectomy (RC). Methods Within the Surveillance, Epidemiology, and End Results database (2004–2020), we identified patients with T 2–4 N 0–3 M 0 bladder cancer treated with RC, who had previously been treated with external beam radiation therapy (EBRT) or brachytherapy for prostate cancer, as well as patients with T 2–4 N 0–3 M 0 pBCa treated with RC. Cumulative incidence plots and multivariable competing risks regression (CRR) models were used to assess CSM after additional adjustment for OCM. The same methodology was then repeated based on organ‐confined (OC: T 2 N 0 M 0 ) and non‐organ‐confined (NOC: T 3–4 and/or N 1–3 ) disease. Results Of 9957 RC patients, RT‐BCa was identified in 347 (3%) compared with 9610 (97%) who had pBCa. In multivariable CRR models, no CSM differences were recorded in the overall comparison ( P = 0.8), nor in sub‐groups based on OC and NOC disease ( P = 0.8 and 0.7, respectively). Conversely, multivariable CRR models identified RT‐BCa as an independent predictor of 1.3‐fold higher OCM in the overall cohort and of 1.5‐fold higher OCM in those with NOC disease. In a sensitivity analysis of patients with NOC disease, EBRT was associated with higher OCM rates (hazard ratio 1.5). By contrast, OCM rates were not different in those with OC disease ( P = 0.8). Conclusion Our study showed that RC for RT‐BCa was associated with similar CSM rates as RC for pBCa, regardless of disease stage. However, patients who had undergone EBRT exhibited significantly higher OCM in the NOC sub‐group.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".