Impact of upstream use of novel hormonal therapy on progression of patients (pts) to metastatic castration-resistant prostate cancer (mCRPC) in the United States (US).
Bibliographic record
Abstract
92 Background: Novel hormonal therapies (nHTs) provide significant delay in disease progression in metastatic castration-sensitive prostate cancer (mCSPC) and non-metastatic (nm)CRPC. The impact of earlier use of these agents on the epidemiological burden of PC in the US was assessed. Methods: A disease stage transition model capturing pt flow through eight clinical states and tracking pt treatment history was used, with each nHT being used once. Progression-free survival (PFS) and overall survival (OS) data for each drug/regimen were derived from published sources where available. We analyzed scenarios 1) no nHTs and 2) with nHTs: (abiraterone acetate + prednisone [AAP], apalutamide [APA] and enzalutamide [ENZA] in high-risk mCSPC, and APA and ENZA in low-risk mCSPC and in high-risk nmCRPC). Resultant state progression parameters were compared, evaluating the impact of nHTs. The assumed nHT utilization was 17.2% in high-risk mCSPC, 7.1% in low-risk mCSPC, and 60.0% in high-risk nmCRPC. Results: For 2018, the model resulted in PC incidence of 240,150 and prevalence of 2,445,173; 49,450 pts progressed to mCRPC, 42% from PSA biochemical recurrence, 31% from mCSPC, and 27% from nmCRPC states. Longer PFS and OS afforded by novel treatments extended the mean time spent from 4.4 to 4.7 yrs in mCSPC and from 2.4 to 3.0 yrs in nmCRPC. This further resulted in reduction in inflow to mCRPC over 2019 – 2025 (table). Conclusions: Novel hormonal therapies are currently used earlier in PC, a trend anticipated to intensify. The disease model shows this change in the treatment paradigm to result in delaying progression to mCRPC and increasing OS in PC.[Table: see text]
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".