Incidence, Characteristics and Survival Rates of Bladder Cancer after Rectosigmoid Cancer Radiation
Bibliographic record
Abstract
Background: Historical external beam radiation therapy (EBRT) for rectosigmoid cancer (RCa) predisposed patients to an increased risk of secondary bladder cancer (BCa). However, no contemporary radiotherapy studies are available. We addressed this knowledge gap. Materials and methods: Within the Surveillance, Epidemiology, and End Results database (2000–2020), we identified non-metastatic RCa patients who either underwent radiotherapy (EBRT+) or did not (EBRT-). Cumulative incidence plots and multivariable competing risk regression models (CRR) were fitted to address rates of BCa after RCa. In the subgroup of BCa patients, the same methodology addressed BCa-specific mortality (BCSM) according to EBRT exposure status. Results: Of the 188,658 non-metastatic RCa patients, 54,562 (29%) were EBRT+ vs. 134,096 (73%) who were EBRT-. In the cumulative incidence plots, the ten-year BCa rates were 0.7% in EBRT+ vs. 0.7% in EBRT- patients (p = 0.8). In the CRR, EBRT+ status was unrelated to BCa rates (multivariable HR: 1.1, p = 0.8). In the subgroup of 1416 patients with BCa after RCa, 443 (31%) were EBRT+ vs. 973 (69%) who were EBRT-. In the cumulative incidence plots, the ten-year BCSM rates were 10.6% in EBRT+ vs. 12.1% in EBRT- patients (p = 0.7). In the CRR, EBRT+ status was unrelated to subsequent BCSM rates (multivariable HR: 0.9, p = 0.9). Conclusion: Although historical EBRT for RCa predisposed patients to higher BCa rates, contemporary EBRT for RCa is not associated with increased subsequent BCa risk. Moreover, in patients with BCa after RCa, exposure to EBRT does not affect BCSM.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".