An appraisal of genetic testing for prostate cancer susceptibility
Bibliographic record
Abstract
Most criteria for genetic testing for prostate cancer susceptibility require a prior diagnosis of prostate cancer, in particular cases with metastatic disease are selected. Advances in the field are expected to improve outcomes through tailored treatments for men with advanced prostate cancer with germline pathogenic variants, although these are not currently offered in the curative setting. A better understanding of the value of genetic testing for prostate cancer susceptibility in screening, for early detection and prevention is necessary. We review and summarize the literature describing germline pathogenic variants in genes associated with increased prostate cancer risk and aggressivity. Important questions include: what is our ability to screen for and prevent prostate cancer in a man with a germline pathogenic variant and how does knowledge of a germline pathogenic variant influence treatment of men with nonmetastatic disease, with hormone-resistant disease and with metastatic disease? The frequency of germline pathogenic variants in prostate cancer is well described, according to personal and family history of cancer and by stage and grade of disease. The role of these genes in aggressive prostate cancer is also discussed. It is timely to consider whether or not genetic testing should be offered to all men with prostate cancer. The goals of testing are to facilitate screening for early cancers in unaffected high-risk men and to prevent advanced disease in men with cancer.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".