Radiation therapy after radical prostatectomy is associated with higher other-cause mortality
Bibliographic record
Abstract
PURPOSE: To test the association between external beam radiotherapy (EBRT) after radical prostatectomy (RP) vs RP only on rates of other-cause mortality (OCM) in men with prostate cancer (PCa). PATIENTS AND METHODS: Within the 2004-2016 Surveillance, Epidemiology, and End Results database, we identified 181,849 localized PCa patients, of whom 168,041 received RP only vs 13,808 who received RP + EBRT. Cumulative incidence plots displayed OCM between RP vs RP + EBRT after propensity score matching for age, PSA, clinical T- and N-stages, and biopsy Gleason scores. Multivariable competing risks regression models addressed OCM, accounting prostate cancer-specific mortality (CSM) as a competing event. Stratifications were made according to low- vs intermediate- vs high-risk groups and additionally according to age groups of ≤ 60, 61-70, and ≥ 71 years, within each risk group. RESULTS: In low-, intermediate-, and high-risk patients, RP + EBRT rates were 2.7, 5.4 and 17.0%, respectively. After matching, 10-year OCM rates between RP and RP + EBRT were 7.7 vs 16.2% in low-, 9.4 vs 13.6% in intermediate-, and 11.4 vs 13.5% in high-risk patients (all p < 0.001), which, respectively, resulted in multivariable HR of 2.1, 1.3, and 1.2 (all p < 0.001). In subgroup analyses, excess OCM was recorded in low-risk RP + EBRT patients of all age groups (all p ≤ 0.03), but only in the older age group in intermediate-risk patients (61-70 years, p = 0.03) and finally, only in the oldest age group in high-risk patients (≥ 71 years, p = 0.02). CONCLUSION: Excess OCM was recorded in patients exposed to RT after RP. Its extent was most pronounced in low-risk patients, decreased in intermediate-risk patients, and was lowest in high-risk patients.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".