Long‐term cancer control outcomes in patients with biochemical recurrence and the impact of time from radical prostatectomy to biochemical recurrence
Bibliographic record
Abstract
BACKGROUND: Rates of metastatic progression (MP) and prostate cancer mortality (PCSM) are variable after biochemical recurrence (BCR) in patients who underwent radical prostatectomy (RP). To describe long-term oncological outcomes of BCR patients and to analyze risk factors for further outcomes in these men with a special focus on RP-BCR time. METHODS: We retrospectively analyzed the data of 5509 RP patients treated between 1992 and 2006. Of those, we included 1321 patients who experienced BCR (PSA level ≥0.2 ng/mL) and did not receive any neoadjuvant or adjuvant therapy. Kaplan-Meier and time dependent Cox regression models were used. RESULTS: Median follow-up was 121 months. MP was recorded in 177 (13.4%), PCSM in 126 (9.5%), and overall mortality (OM) in 264 (20.0%) patients. Patients with MP had worse tumor characteristics such as higher Gleason Scores (GS), rapid PSA doubling-time (DT), and shorter RP-BCR time intervals. MP-free, PCSM-free, and overall survival rates were significantly worse in patients with RP-BCR time of <12 months versus patients with 12-35.9 or ≥36 months (P ≤ 0.001). Besides higher GS and rapid PSA-DT, RP-BCR time independently predicted MP, PCSM, and OM in multivariable regression analyses. Relative to the intermediate and longest RP-BCR time interval, the shortest interval (<12) carried the highest risk for all three endpoints. CONCLUSIONS: Only a small proportion of BCR patients proceed to MP or PCSM. Besides higher GS and rapid PSA-DT a shorter RP-BCR interval (<12 months) heralds the most aggressive phenotype for progression to all three examined endpoints: MP, PCSM, and OM.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".