Management algorithms for metastatic prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer poses a significant lifetime risk to Canadian men. Treatment for metastatic prostatic cancer (mPCa) is an area of ongoing research with a lack of up-to-date clinical guidance. The multidisciplinary Canadian Genitourinary Research Consortium (GURC) determined that additional guidance focusing on management of mPCa was warranted. METHODS: The most up-to-date guidelines, consensus statements, and emerging phase 3 trials were identified and used to inform development of algorithms by a multidisciplinary genitourinary oncology panel outlining recommendations for the management of mPCa. RESULTS: A single pan-Canadian guideline and five national and international guidelines or consensus statements published since 2015 were identified, along with two new phase 3 trials and one additional randomized comparison. Iterative GURC discussions led to the development of two mPCa algorithms: the first addressing management of newly diagnosed metastatic castration-sensitive prostate cancer (mCSPC) patients and the second addressing treatment of patients with metastatic castration-resistant prostate cancer (mCRPC). For newly diagnosed mCSPC patients with high-volume/high-risk disease, either docetaxel or abiraterone acetate and prednisone (AAP) added to androgen-deprivation therapy (ADT) is recommended. The addition of radiotherapy to ADT is suggested for those with low-volume disease and/or AAP to ADT for low-volume or low-risk disease. For first-line mCRPC, androgen receptor-axis-targeted (ARAT) therapy is recommended for most patients, while sequencing with docetaxel, radium-223, ARAT therapy, and/or cabazitaxel is recommended for later lines of therapy. CONCLUSIONS: Two treatment algorithms were developed for the management of mPC and can be used by multidisciplinary specialist teams to guide treatment.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".