Percent free prostate‐specific antigen (PSA) is an accurate predictor of prostate cancer risk in men with serum PSA 2.5 ng/mL and lower
Bibliographic record
Abstract
BACKGROUND: Up to 17% of men with a prostate-specific antigen (PSA) level below the accepted prostate biopsy cutoff of 2.5 ng/mL may have prostate cancer. Because identification of these patients represents a difficult task, we assessed the ability of percent free PSA to discriminate between benign and malignant prostate biopsy outcomes in men with PSA < or =2.5 ng/mL. METHODS: Between 1999 and 2006, 543 men with a PSA < or =2.5 ng/mL were referred for initial prostate biopsy. Age, total PSA, percent free PSA, and digital rectal examination findings represented predictors of prostate cancer at biopsy in logistic regression models. The area under the receiver operating characteristics curve (AUC) quantified the discriminative ability of the predictors. The pathological characteristics of the detected cancers were assessed in individuals treated with radical prostatectomy. RESULTS: Of all, 23% had prostate cancer on biopsy, 16.5% of patients treated with radical prostatectomy had pT3 stage, and 35.6% had a pathological Gleason score of 3 + 4 or higher. The most accurate predictor of prostate cancer on biopsy was percent free PSA (0.68) versus age (0.50), total PSA (0.57), or rectal examination findings (0.58). Of patients with percent free PSA below 14%, 59% had prostate cancer. In multivariate models, percent free PSA (P < .001) and rectal examination findings (P = .001) were the only independent predictors of prostate cancer. The combined AUC of all predictors (0.69) was not significantly (P = .7) higher than that of percentage of free PSA alone (0.68). CONCLUSIONS: The risk of prostate cancer is clearly non-negligible in patients with PSA < or =2.5 ng/mL. The percent free PSA can accurately predict the prevalence of prostate cancer at prostate biopsy in these individuals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".