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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".