Finding the optimal treatment sequence in metastatic castration-resistant prostate cancer—a narrative review
Bibliographic record
Abstract
Over the last two decades, there has been significant progress in the treatment of metastatic prostate cancer. Multiple treatments with diverse mechanisms of action have improved clinical outcomes for patients with metastatic castration-resistant prostate cancer (mCRPC) including taxane chemotherapy, immunotherapy, potent androgen receptor pathway inhibitors (ARPI), and radiopharmaceuticals (radium-223). As these treatments have entered standard clinical practise, clinicians have been challenged on how to optimally select and sequence them as the landmark studies establishing their efficacy had control arms with placebo or minimally effective therapy and there is a paucity of prospective trials examining treatment sequencing. More recently, the situation has been further complicated as the earlier up-front use of docetaxel and ARPI with standard gonadal testosterone inhibition has been shown to impart substantial improvements in disease control and survival for patients with castration sensitive prostate cancer. As new therapies enter into clinical practise such as the inhibitors of Poly (ADP-Ribose) Polymerase and Prostate Specific Membrane Antigen (PSMA)-targeted therapy, how to optimally select and sequence available treatments will be a continued dilemma in the absence of validated predictive biomarkers. This review will summarize the literature supporting the use of each active agent in mCRPC. We will propose a framework which will guide the selection of appropriate agents based on prior therapies, disease characteristics and biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".