Urinary PSA: a potential useful marker when serum PSA is between 2.5 ng/mL and 10 ng/mL
Bibliographic record
Abstract
INTRODUCTION: Our objective was to evaluate the usefulness of urinary prostate specific antigen (PSA) in the differential diagnosis of benign prostatic hyperplasia (BPH) and prostate cancer. METHODS: We undertook a prospective study and obtained informed consent from 170 men. They provided blood samples to measure serum PSA and 50 mL of first-voided urine to measure urinary PSA. Seventy-seven men were diagnosed with BPH; 42 patients had newly diagnosed prostate cancer; and 51 were selected as age-matched control subjects. Data were analyzed using Wilcoxon signed rank tests, receiver operating characteristic (ROC) curves and logistic regression. RESULTS: Prostate volume was 35 cm(3) and 45 cm(3) (p < 0.05), serum PSA was 9.7 ng/mL and 4.5 ng/mL (p < 0.001) and PSA density was 0.28 and 0.11 (p < 0.01) for prostate cancer and BPH patients, respectively. Overall, urinary PSA was not significantly different, but PSA ratio (urinary:serum) was significantly different at 6.7 and 30.6 (p < 0.001) for prostate cancer and BPH patients, respectively. A subgroup with serum PSA between 2.5 ng/mL and 10.0 ng/mL was selected and urinary PSA was significant: 52.6 ng/mL (n = 29) and 123.2 ng/mL (n = 35) (p < 0.05) for prostate cancer and BPH patients, respectively. PSA ratios were also significant (p = 0.007). ROC curves identified a cutoff for urinary PSA at > 150 ng/mL, with a sensitivity of 92.5%. When comparing prostate cancer patients with age-matched control subjects, serum PSA, urinary PSA and PSA ratio were different (p = 0.004). CONCLUSION: Our study supports urinary PSA as a useful marker in the differential diagnosis of prostate cancer and BPH, especially when serum PSA is between 2.5 ng/mL and 10 ng/mL. Low urinary PSA and PSA ratios point toward prostate cancer. A urinary PSA threshold of > 150 ng/mL may be used to decrease the number of prostatic biopsies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".