Targeting Bone Metabolism in Patients with Advanced Prostate Cancer: Current Options and Controversies
Bibliographic record
Abstract
Maintaining bone health remains a clinical challenge in patients with prostate cancer (PC) who are at risk of developing metastatic bone disease and increased bone loss due to hormone ablation therapy. In patients with cancer-treatment induced bone loss (CTIBL), antiresorptive agents have been shown to improve bone mineral density (BMD) and to reduce the risk of fractures. For patients with bone metastases, both zoledronic acid and denosumab delay skeletal related events (SREs) in the castration resistant stage of disease. Novel agents targeting the Wnt inhibitors dickkopf-1 and sclerostin are currently under investigation for the treatment of osteoporosis and malignant bone disease. New antineoplastic drugs such as abiraterone, enzalutamide, and Radium-223 are capable of further delaying SREs in patients with advanced PC. The benefit of antiresorptive treatment for patients with castration sensitive PC appears to be limited. Recent trials on the use of zoledronic acid for the prevention of bone metastases failed to be successful, whereas denosumab delayed the occurrence of bone metastases by a median of 4.1 months. Currently, the use of antiresorptive drugs to prevent bone metastases still remains a field of controversies and further trials are needed to identify patient subgroups that may profit from early therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".