Can free PSA be used as a biomarker in biochemical recurrence after surgery to predict castrate resistant prostate cancer?
Bibliographic record
Abstract
177 Background: PSA produced from prostate cancer (PC) cells escapes proteolytic processing, resulting in a more complexed PSA and a lower %fPSA. Higher %fpsa correlates with lower PC risk. However, the role of fPSA in biochemical recurrence (BCR) after radical prostatectomy (RP) is unknown. Methods: All patients who had BCR after RP and at least one fPSA test, were included. Patients were stratified according to the %fPSA cut-off of 0.15. Multivariable logistic regression analysis was performed to predict covariates associated with a higher %fPSA. Results: A total of 81 men with BCR were found (Table 1). Interestingly, 20% (group 1) vs. 60% (groups 2) become castrate resistant (CRPC), p<0.0001 and the time to reach CRPC state was much shorter in group 2 (33.5 months) vs. group 1 (57.9 months), p=0.05. Additionally, 60% of group 2 patients vs. 32.5% of group 1 patients developed metastasis, p=0.014. Lastly, median survival of 193 months for group 2 patients with no median survival for group 1, Log Rank test p=0.023. Multivariable logistic regression analysis demonstrated that secondary Gleason score of 5 (compared to 3) and %fPSA>0.15 predicted CRPC status (OR 11.63, CI 95% 1.38-97.4, p=0.024, OR 7.99, CI 95% 2-31.95, p=0.003, respectively). Conclusions: %fPSA>0.15 in the setting of BCR confers a more aggressive disease, manifesting in a faster development of CRPC, metastasis and death. Our findings suggest a reversal in the significance of % fPSA values in BCR patients, and should be validated in larger cohorts. [Table: see text]
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".