Personalization of prostate cancer prevention and therapy: are clinically qualified biomarkers in the horizon?
Bibliographic record
Abstract
Prostate cancer remains the most common malignancy among men and the second leading cause of male cancer-related mortality. Death from this disease is invariably due to resistance to androgen deprivation therapy. Our improved understanding of the biology of prostate cancer has heralded a new era in molecular anticancer drug development, with multiple novel anticancer drugs for castration resistant prostate cancer now entering the clinic. These include the taxane cabazitaxel, the vaccine sipuleucel-T, the CYP17 inhibitor abiraterone, the novel androgen receptor antagonist MDV-3100 and the radionuclide alpharadin. The management and therapeutic landscape of prostate cancer has now been transformed with this growing armamentarium of effective antitumor agents. This review discusses strategies for the prevention and personalization of prostate cancer therapy, with a focus on the development of predictive and intermediate endpoint biomarkers, as well as novel clinical trial designs that will be crucial for the optimal development of such anticancer therapeutics.
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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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".