Cancer-specific mortality after radical prostatectomy versus radiotherapy in incidental prostate cancer
Bibliographic record
Abstract
INTRODUCTION: To test for cancer specific mortality (CSM) differences after either radical prostatectomy (RP) or radiotherapy (RT) in incidental prostate cancer (IPCa) patients. PATIENTS AND METHODS: Within the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015), IPCa patients were identified. Cumulative incidence plots as well as competing risks regression (CRR) models were fitted to address CSM after adjustment for other-cause mortality (OCM). Furthermore, a subgroup analysis was performed to test for CSM differences between RP and RT according to Gleason sum (GS 6,7, and 8-10). RESULTS: Of 1,466 IPCa patients, 770 (53%) underwent RP vs. 696 (47%) RT. Incidental PCa RT patients were older, and exhibited higher PSA, higher proportion of Gleason sum 8-10, and higher clinical T stage. In cumulative incidence plots, 5-year CSM rates adjusted for OCM were 0.9 for RP vs. 6.8% for RT (Δ = 5.9%). After multivariable adjustment for clinical characteristics (age, PSA, Gleason sum, and clinical T stage) as well as for OCM, RP was associated with a protective hazard ratio (HR) of 0.35 (95% confidence interval [CI] 0.15 - 0.78, p value = .01). Within Gleason sum 8-10 IPCA patients, RP was associated with a protective HR of 0.31 (P = .039). CONCLUSION: Incidental PCa RT-treated patients exhibited less favorable clinical characteristics than their RP counterparts. Despite full adjustment, RP was associated with a protective effect relative to RT. This effect exclusively applied to the Gleason sum 8-10 subgroup. In consequence, IPCa patients harboring Gleason sum 8-10 should ideally be considered for RP instead of RT.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".