Long‐term incidence of secondary bladder and rectal cancer in patients treated with brachytherapy for localized prostate cancer: a large‐scale population‐based analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the incidence and time trends of secondary bladder cancer (BCa) and rectal cancer (RCa) after brachytherapy (BT) relative to radical prostatectomy (RP). MATERIALS AND METHODS: Within the Surveillance, Epidemiology and End Results (SEER) database (1988-2015), we identified patients with localized PCa as an only or first primary cancer, who underwent BT or RP. Cumulative incidence plots and multivariable competing-risks regression (CRR) models were used. Sensitivity analyses focused on patients' age and year of diagnosis intervals and tested the effect of an unmeasured confounder. RESULTS: Of 318 058 patients with localized prostate cancer (PCa), 55 566 (18.4%) underwent BT. After propensity score-matching, 20-year secondary BCa incidence was 6.0% in patients who had undergone BT vs 2.4% in those who had undergone RP (P < 0.001) and the respective 20-year secondary RCa incidence was 1.1% vs 0.5% (P < 0.001). In multivariable CRR models, BT predicted higher secondary BCa (hazard ratio [HR] 1.58; P < 0.001) and RCa rates (HR 1.59; P < 0.001) vs RP. Sensitivity analyses replicated the same results after stratification according to age and showed HRs of decreasing magnitude for historical, intermediate and contemporary years of diagnosis. An unmeasured confounder with an HR of 2 would render the effect of BT statistically insignificant if it affected patients in the RP group with a ratio of 2 relative to those in the BT group. Finally, temporal trends showed a decrease of secondary 5-year BCa and RCa rates.> CONCLUSIONS: Brachytherapy predominantly increases the risk of secondary BCa and, to a lesser extent, that of RCa. Follow-up of such patients is therefore required. It is encouraging that both secondary BCa, and RCa rates, in particular, have recently decreased, RCa.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".