Making Sense of Prostate Specific Antigen: Improving its Predictive Value in Patients Undergoing Prostate Biopsy
Bibliographic record
Abstract
PURPOSE: The clinical usefulness of PSA for prostate cancer screening is unclear, although the test remains in common use. New methods to interpret PSA are needed. MATERIALS AND METHODS: We examined a cohort of 2,637 men who underwent prostate biopsies for abnormal DRE or PSA between 1999 and 2004. Using risk factors for prostate cancer, including patient age, ethnicity, family history of prostate cancer, previous negative biopsy, voiding symptoms and prostate volume, we developed risk groups for prostate cancer using recursive partitioning modeling independent of PSA or DRE. We then compared prostate cancer probabilities by PSA ranges by risk group. RESULTS: Of the 2,637 men 1,282 (48.6%) had prostate cancer. Age, ethnicity, family history, previous negative biopsy and prostate volume were predictive for cancer. We constructed 6 risk groups by combining these factors and created tables to assign patients to these groups. Independent of PSA and DRE the probability of cancer ranged from 15% in patients in group 1 to 78% in patients in group 6 (p <0.0001). By adding PSA and DRE to each risk group prostate cancer probabilities were refined from 0% to 100%. Patients in the higher risk groups also had higher grade cancer (p <0.0001). CONCLUSIONS: We generated 6 risk groups based on simple risk factors for prostate cancer. When used in the right context and patient, PSA is highly accurate for predicting prostate cancer and permitting rational decision making in patients with abnormal PSA.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".