A Framework for the Identification of Men at Increased Risk for Prostate Cancer
Bibliographic record
Abstract
PURPOSE: We assessed the risk of prostate cancer over time, and the implications for screening strategies and potential risk reduction approaches to provide a framework for clinical use of this approach concordant with the use of prostate specific antigen as a marker of current prostate cancer risk. MATERIALS AND METHODS: A comprehensive review of the relevant literature was performed. In this article the phrase risk of/for prostate cancer refers to the risk of developing prostate cancer. RESULTS: Prostate specific antigen is the single most significant predictive factor for identifying men at increased risk for prostate cancer. A suspicious digital rectal examination, a family history of prostate cancer, the presence of high grade prostatic intraepithelial neoplasia or atypical small acinar proliferation and black ethnicity are also important predictive factors, while larger prostate volume and a previous negative biopsy are negative predictors. For men of screening age (50 to 70 years) a prostate specific antigen of greater than 1.5 ng/ml is a marker for greater than average risk up to 8 years (7.5-times greater risk vs 1.5 ng/ml or less). This prostate specific antigen threshold for a man at above average risk can be modified by the presence of other predictive factors. It should be lower for men with a prostate volume less than 40 cc, black ethnicity or a family history of prostate cancer. For younger men with longer followup a lower prostate specific antigen may be considered. CONCLUSIONS: The risk of prostate cancer can be estimated in individual men primarily using prostate specific antigen, but also using prostate volume, previous biopsy status, family history and ethnicity. Men at increased risk warrant enhanced surveillance and in the future may also be candidates for active risk reduction strategies.
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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 | 0.034 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".