The changing landscape in metastatic castration-resistant prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: The treatment landscape in metastatic castration-resistant prostate cancer (mCRPC) has significantly changed in the recent years. We provide an updated summary of the new therapeutic agents in this disease and discuss open questions and future challenges. RECENT FINDINGS: mCRPC is now known to frequently retain sensitivity to hormonal manipulation even after the development of castration resistance, and both the androgen synthesis inhibitor abiraterone and the androgen-receptor antagonist enzalutamide have recently shown to prolong survival in mCRPC patients after chemotherapy. Cabazitaxel, a new-generation antitubulin chemotherapeutic, and the radionuclide radium-223 chloride have also been shown to prolong survival. The biological agent cabozantinib, an orally bioavailable tyrosine kinase inhibitor with activity against Met and vascular endothelial growth factor receptor 2, demonstrated promising results in a phase II trial and is currently being assessed in two large randomized phase 3 controlled trials. SUMMARY: This recent progress is unprecedented and has already translated to a significant increase in the available armamentarium of drugs for mCRPC. Nonetheless, there are still significant unresolved questions as to the proper sequencing of these novel drugs along the disease continuum. Moreover, the problem of drug resistance, either primary of acquired, continues to be a major therapeutic obstacle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".