The evolving role of germline genetic testing and management in prostate cancer: Report from the Princess Margaret Cancer Centre International Retreat
Bibliographic record
Abstract
Prostate cancer is a significant cause of cancer mortality. It has been well-established that certain germline pathogenic variants confer both an increased risk of being diagnosed with prostate cancer and dying of prostate cancer.1 There are exciting developments in both the availability of genetic testing and opportunities for improved treatment of patients.On August 19, 2020, the Princess Margaret Cancer Centre in Toronto, Ontario, hosted a virtual retreat, bringing together international experts in urology, medical oncology, radiation oncology, medical genetics, and translational research, as well as a patient representative. We are pleased to provide this manuscript as a review of those proceedings for Canadian clinicians.We highlighted several needs for future research and policy action based on this meeting:Increased access to funding for germline testing for the common genetic disorders associated with increased risk of prostate cancer.More research into identifying genetic factors influencing risk stratification, treatment response, and outcomes of prostate cancer within Canadian populations at higher genetic risk for prostate cancer.Added awareness about genetic risk factors among the Canadian public.Development of patient-specific and reported outcomes research in tailored care for patients at increased genetic risk of prostate cancer.Creation of multidisciplinary clinics that specialize in tailored care for patients at increased genetic risk of prostate 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".