Utility of PSA free‐to‐total ratio for clinically significant prostate cancer in men with a PSA level of <4 ng/mL
Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between the prostate-specific antigen (PSA) free-to-total ratio (FTR) and International Society of Urological Pathology Grade Group ≥2, clinically significant prostate cancer (csPCa) in men with a low PSA level (≤4 ng/mL). Patients and Methods Data were obtained from the Prostate Cancer Prevention Trial. Patients with a PSA level of ≤4 ng/mL and who received a biopsy within a year of this PSA measurement were included. Associations between FTR and csPCa were investigated with logistic regression, adjusting for age and PSA, a re-scaled Brier score (index of predictive accuracy), and decision curve analysis. RESULTS: A total of 406 patients were analysed with 139 (34%) having csPCa and 204 (50%) having any grade PCa. For those with an FTR ≤0.15, 46% had csPCa, vs 22% for those with a ratio ≥0.20. In a regression model, the predicted probability of csPCa for a 60-year-old with a PSA of 3 ng/mL was 61% if the FTR was 0.05, falling to 18% if the FTR was 0.30. A clear negative relationship between increasing FTR and probability of csPCa was observed. A model containing FTR additional to PSA and age provides greater net benefit as per decision curve analysis and likely superior discrimination and calibration measured by a higher index of predictive accuracy. CONCLUSIONS: In middle-aged men with a PSA level between 1.5 and 4 ng/mL but otherwise indicated for biopsy, a low FTR is associated with higher rates of csPCa. It should be utilised as an additional, readily available and inexpensive test to improve prediction of csPCa and aid in patient counselling.
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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 | 0.004 | 0.021 |
| 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 | 0.001 |
| 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".