Evaluating the impact of bone-targeted agents in the era of novel androgen targeted therapy for metastatic castration-resistant prostate cancer.
Bibliographic record
Abstract
e16500 Background: Patients with metastatic castrate resistant prostate cancer (mCRPC) often develop bone metastases, resulting in a risk of symptomatic skeletal events (SSE). Bone-targeted agent (BTA) has been integrated into the overall treatment strategy, but its role in the era of novel androgen-targeted therapy (ATT) is still unclear. Methods: A retrospective analysis of real-world practice from 2010 to 2015 was conducted. Patients diagnosed with mCRPC and bone metastases who received systemic therapy (docetaxel/abiraterone/enzalutamide) with or without BTA (zoledronic acid/denosumab/alendronate) were included. Results: We obtained data from 299 patients with a median follow-up of 75.5 months. Compared with no BTA, concomitant BTA was associated with decreased incident of SSE (3.8% vs. 19.2%, p< 0.001), especially in the need for bone radiation (1.9% vs. 15.8%, p< 0.001). Compared to patients without SSE, patients with SSE were more likely to have previous fracture history (10.8% vs. 9.7%, p= 0.047), steroid use history (2.4% vs. 1.4%, p= 0.019), smoking history (53.0% vs. 38.9%, p= 0.009), diabetes (19.3% vs. 8.8%, p= 0.012) and reduced baseline mobility (9.6% vs. 4.2%, p= 0.002). Among those who received first-line novel ATT (n = 152), concurrent ATT+BTA significantly decreased the incident of SSE (2.8% vs. 27.3%, p= 0.004) and prolonged the time to SSE (24.3 vs. 3.2 mo, p< 0.001), when compared to ATT alone. But there was no difference in overall survival between two groups (134.2 vs. 138.9 mo, p= 0.99). In patients who responded to systemic treatment with PSA declining, the SSE rate (2.4% vs. 14.3%, p= 0.037) was lower and the time to SSE was longer (49.6 vs. 32.3 mo, p= 0.026) than those who did not respond. Smoking history (adjusted HR = 1.46, p= 0.013) and PSA response (adjusted HR = 0.19, p< 0.001) are independent risk factors for SSE. Conclusions: This real-world database suggests that concomitant BTA with novel ATT was associated with reducing SSEs. PSA decrease might be used as a predictive factor for those under BTA treatment. Further studies are warranted to validate the role of PSA response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".