Systemic therapy for nonmetastatic castrate-resistant prostate cancer (M0 CRPC): A systematic review and network meta-analysis (NMA).
Bibliographic record
Abstract
113 Background: The treatment landscape for M0 CRPC has changed following the demonstrated efficacy of new agents in recent randomized control trials (RCT). However, the comparative effectiveness of these novel agents is unknown. This NMA indirectly compared the efficacy and safety of available therapies for M0 CRPC. Methods: A literature search of MEDLINE (Ovid), EBM Reviews, HealthSTAR, PubMed, PubMed Central, CINAHL, and TRIP Database was performed. Studies were screened by two independent reviewers. Hazard ratios (HR) and confidence intervals were extracted for the primary outcome metastasis-free survival (MFS) and the secondary outcomes overall survival (OS) and grade 3 or higher adverse events (AE). Bone MFS was used as a surrogate for MFS when MFS was not reported. Risk of bias was assessed using the Cochrane Collaboration tool. A Bayesian NMA was performed using a fixed-effects model. Results: Four RCT were analyzed (n=5549). Each trial compared either apalutamide (APA), enzalutamide (ENZA), darolutamide (DARO), or denosumab (DENO) plus androgen deprivation therapy (ADT) to placebo plus ADT. Risk of bias was low. For MFS, APA and ENZA had similar efficacy (Table), and Surface Under the Cumulative Ranking Analysis demonstrated a 59% probability that APA was preferred for MFS, followed by ENZA (41%). There was a trend for improved OS for APA, DARO and ENZA, but no meaningful differences between these agents. APA, ENZA, and DARO had a similar risk of AEs and all had a greater risk of AEs compared to DENO. Conclusions: APA and ENZA appear to be the most efficacious treatments for MFS in M0 CRPC, though more data for OS is required. Compared to DARO, APA and ENZA’s demonstrated efficacy is not at the expense of added toxicity.[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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".