Time from first detectable PSA following radical prostatectomy to biochemical recurrence: A competing risk analysis
Bibliographic record
Abstract
INTRODUCTION: In this study, we estimated the time from first detectable prostate-specific antigen (PSA) following radical prostatectomy (RP) to commonly used definitions of biochemical recurrence (BCR). We also identified the predictors of time to BCR. METHODS: We identified subjects who underwent a RP and had an undetectable PSA after surgery followed by at least 1 detectable PSA between 2000 and 2011. The primary outcome was time to BCR (PSA ≥0.2 and successive PSA ≥0.2) and prediction of the rate of PSA rise. Outcomes were calculated using a competing risk analysis, with univariable and multivariable Fine and Grey models. We employed a mixed effect model to test clinical predictors that are associated with the rate of PSA rise. RESULTS: The cohort included 376 patients. The median follow-up from surgery was 60.5 months (interquartile range [IQR] 40.8-91.5) and from detectable PSA was 18 months (IQR 11-32). Only 45.74% (n = 172) had PSA values ≥0.2 ng/mL, while 15.16% (n = 57) reached the PSA level of ≥0.4 ng/mL and rising. On multivariable analysis, the values of the first detectable PSA and pathologic Gleason grade 8 or higher were consistently independent predictors of time to BCR. In the mixed effect model rate, the PSA rise was associated with time from surgery to first detectable PSA, Gleason score, and prostate volume. The main limitation of this study is the large proportion of patients that received treatment without reaching BCR. It is plausible that shorter estimated median times would occur at a centre that does not use salvage therapy at such an early state. CONCLUSION: The time from first detectable PSA to BCR may be lengthy. Our analyses of the predictors of the rate of PSA rise can help determine a personalized approach for patients with a detectable PSA after surgery.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".