Comparative evaluation of various prostate specific antigen ratios for the early detection of prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To compare the performance of various ratios using total prostate specific antigen (PSA), complexed PSA (cPSA) and free PSA (fPSA) in the early detection of prostate cancer. PATIENTS AND METHODS: The study included 535 consecutive patients evaluated at a prostate cancer detection clinic between January 1998 and October 1999. Patients had blood samples drawn before transrectal ultrasonography and prostate biopsy to measure PSA, cPSA and fPSA. Receiver operating characteristic (ROC) curves (sensitivity vs 1 - specificity) were used to evaluate the performance of PSA, cPSA, f/tPSA, cPSA/tPSA, fPSA/cPSA, tPSA/prostate volume (PV), fPSA/PV, and cPSA/PV. The areas under the curve (AUC) were calculated for each ratio. The performance of each ratio over all patients or in those with a tPSA of 4-6 or 4-10 ng/mL were evaluated. RESULTS: Of the 535 patients, 204 (38%) had biopsy-confirmed prostate cancer. The AUC obtained with tPSA alone was 0.64; when measured for all patients the cPSA/PV (0.78), PSA/PV (0.77), f/tPSA (0.76) and fPSA/cPSA (0.75) performed better than tPSA alone. Furthermore, in patients with a tPSA of 4-10 ng/mL, tPSA/PV (0.72), cPSA/PV (0.71), f/tPSA (0.69), fPSA/cPSA (0.69) and cPSA/tPSA (0.62) performed better than tPSA alone (0.52). Finally, in patients with a tPSA of 4-6 ng/mL, PSA/PV and cPSA/PV performed better than the other ratios. CONCLUSIONS: The use of PSA ratios gives a higher sensitivity and specificity for detecting prostate cancer than the use of tPSA alone.
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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 | 0.006 | 0.022 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".