Can post‐treatment free PSA ratio be used to predict adverse outcomes in recurrent prostate cancer?
Bibliographic record
Abstract
OBJECTIVES: To assess whether free PSA ratio (FPSAR) at biochemical recurrence (BCR) can predict metastasis, castrate-resistant prostate cancer (CRPC), and cancer-specific survival (CSS), following therapy for localised disease. PATIENTS AND METHODS: A single-centre retrospective cohort study (NCT03927287) including a discovery cohort composed of patients with an FPSAR after radical prostatectomy (RP) or radiotherapy (RT) between 2000 and 2017. For validation, an independent Biobank cohort of patients with BCR after RP was tested. Using a defined FPSAR cut-off, the metastasis-free-survival (MFS), CRPC-free survival, and CSS were compared. Multivariable Cox models determined the association between post-treatment FPSAR, metastases, and CRPC. RESULTS: Overall, 822 patients (305 RP- and 363 RT-treated patients and 154 Biobank patients) were analysed. In the RP cohort, a total of 272/305 (89.1%) and 33/305 (10.9%) had a FPSAR test incidentally and reflexively, respectively. In the RT cohort, 155/363 (42.7%) and 208/263 (57.3%) had a FPSAR test incidentally and reflexively, respectively. However, in the prospective Biobank RP cohort, FPSAR testing was done on all samples of patients diagnosed with BCR. A FPSAR cut-off of 0.10 was determined using receiver operating characteristic analyses in both the RP and RT cohorts. A FPSAR of <0.10 resulted in longer median MFS (14.8 vs 9.3 years and 14.8 vs 13 years, respectively), and longer median CRPC-free survival (median not reached vs 9.9 years and 20.7 vs 13.8 years, respectively). Multivariable analyses showed that a FPSAR of ≥0.10 was associated with increased metastasis in the RP cohort (hazard ratio [HR] 1.915, 95% confidence interval [CI] 1.241-2.955) and RT cohort (HR 1.754, 95% CI 1.112-2.769), and increased CRPC in the RP cohort (HR 2.470, 95% CI 1.493-4.088). Findings were validated in the Biobank cohort. CONCLUSIONS: A post-treatment FPSAR of ≥0.10 is associated with more aggressive disease, suggesting a potentially novel role for this biomarker.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".