Genetic testing practices among specialist physicians who treat prostate cancer
Bibliographic record
Abstract
INTRODUCTION: In patients with prostate cancer (PCa), the identification of an alteration in genes associated with homologous recombination repair (HRR) has implications for prognostication, optimization of therapy, and familial risk mitigation. The aim of this study was to assess the genomic testing landscape of PCa in Canada and to recommend an approach to offering germline and tumor testing for HRR-associated genes. METHODS: The Canadian Genitourinary Research Consortium (GURC) administered a cross-sectional survey to a largely academic, multidisciplinary group of investigators across 22 GURC sites between January and June 2022. RESULTS: Thirty-eight investigators from all 22 sites responded to the survey. Germline genetic testing was initiated by 34%, while 45% required a referral to a genetic specialist. Most investigators (82%) reported that both germline and tumor testing were needed, with 92% currently offering germline and 72% offering tissue testing to patients with advanced PCa. The most cited reasons for not offering testing were an access gap (50%), uncertainties around who to test and which genes to test, (33%) and interpreting results (17%). A majority reported that patients with advanced PCa (74-80%) should be tested, with few investigators testing patients with localized disease except when there is a family history of PCa (45-55%). CONCLUSIONS: Canadian physicians with academic subspecialist backgrounds in genitourinary malignancies recognize the benefits of both germline and somatic testing in PCa; however, there are challenges in accessing testing across practices and specialties. An algorithm to reduce uncertainty for providers when ordering genetic testing for patients with PCa is proposed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".