Impact of intervening in high-risk nonmetastatic castration-resistant prostate cancer (HRnmCRPC) on metastatic castration-resistant prostate cancer (mCRPC) disease burden.
Bibliographic record
Abstract
e17010 Background: Approved treatments for patients with mCRPC are associated with meaningful improvements in progression-free and overall survival. The burden of mCRPC remains high, however, as it is linked to high mortality and cost. In the randomized placebo-controlled phase 3 SPARTAN trial, apalutamide, an orally administered next-generation androgen receptor inhibitor, demonstrated clinically significant improvement in metastasis-free survival, with a hazard ratio of 0.28 (95% CI, 0.23-0.35) for patients with HRnmCRPC. This study explores the potential epidemiologic impact of apalutamide in HRnmCRPC and mCRPC. Methods: A published US dynamic disease progression model for prostate cancer was updated, incorporating clinical trial data from new mCRPC and HRnmCRPC treatments. The analyses focused on progression of patients from HRnmCRPC to mCRPC with current treatments (Base Case) and upon introduction of apalutamide. Quality-adjusted life years (QALYs) were estimated based on EQ-5D utility reported in SPARTAN and the literature. Results: With 50% of incident nmCRPC patients considered high risk, the model estimates 2018 US prevalence of HRnmCRPC at 31,682. Each year, 39% of HRnmCRPC patients progress to mCRPC. Without the introduction of new treatments, the number of new patients progressing from HRnmCRPC to mCRPC between 2018 and 2024 is projected to be 89,015. With the introduction of apalutamide, the cumulative incident of mCRPC cases averted will be 33,827, which represents a 38% reduction. The cumulative deaths averted from 2018 to 2024 will be 92,553, and a total of 88,682 QALYs will be gained during this 7-year period. Conclusions: Using a dynamic disease state model, introducing effective novel treatment in nmCRPC delays or prevents the progression from HRnmCRPC to mCRPC, and leads to considerable reduction in the clinical burden associated with mCRPC.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".