Real-world management of metastatic castration-resistant prostate cancer (mCRPC): A national multicenter cohort study.
Bibliographic record
Abstract
252 Background: The management of patients with mCRPC has evolved since the introduction of androgen-receptor axis targeted agents (ARATs). The Genitourinary Research Consortium (GURC) initiated a prospective, phase 4, multicentre, non-interventional, longitudinal cohort study of Canadian men with advanced prostate cancer to determine real-world treatment patterns and outcomes. Methods: 25 sites across Canada participated in this study including patients managed by urologists, medical- and radiation-oncologists between 2018 to 2021. Baseline patient characteristics and mCRPC treatment patterns are reported here. Treatment patterns reviewed included time to second-line treatment use and time to progression or death. Results: 136 mCRPC patients were enrolled. Median age was 73 years (range 66 to 80) with 54 (40%) having a Gleason score of >8 at diagnosis. Median PSA at enrollment was 8.9 (2.4 to 25.1) ng/ml. At study entry, 90/132 (66%) patients with mCSPC and 42/132 (31%) patients nmCRPC had progressed to develop mCRPC. One hundred and twenty-one (89%) of patients in this cohort received first-line treatment for mCRPC, the most common was abiraterone acetate + prednisone in 67 (49%) and enzalutamide in 41 (30%), followed by docetaxel in 6 (4.4%), and Radium-223 in 5 (3.7%) patients. During the 25-month median follow-up period (range 6-28), 59 (49%) of the patients receiving first line mCRPC therapy had documented disease progression or death. At the time of last recorded follow-up, 37 (28%) patients who progressed received a second-line therapy for mCRPC. Median time to progression in this cohort was 21 months (95% CI: 15.2 - NE), with ARAT-to-ARAT being the most common sequencing pattern observed in 15 (39%) patients, followed by ARAT to chemotherapy in 14 (37%). Conclusions: In this real-world analysis of mCRPC patients, ARAT therapy was the preferred approach for first-line treatment intensification in over 108 (80%) patients. Despite evidence of poor response rates, ARAT-to-ARAT was the most common sequencing for second line therapy, followed by ARAT-to-chemotherapy treatment. Further analysis and follow-up will help define optimal mCRPC management, in real world setting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".