Triplet or Doublet Therapy in Metastatic Hormone-sensitive Prostate Cancer Patients: An Updated Network Meta-analysis Including ARANOTE Data
Bibliographic record
Abstract
The treatment landscape for metastatic hormone-sensitive prostate cancer (mHSPC) has been extended by another phase 3 randomized control trial (ARANOTE) demonstrating favorable outcomes of a doublet therapy combining the androgen receptor pathway inhibitor (ARPI) darolutamide with androgen deprivation therapy (ADT) over ADT monotherapy. Owing to differences in trial designs, patient enrollment, and most notably different control treatment regimens, we hereby present an updated network meta-analysis (NMA) embedding the doublet therapy with darolutamide within the current treatment regimens. In NMA-derived ranking, darolutamide and ADT showed similar oncological efficacy to the already known doublet therapies for progression-free survival (p = 0.49). These findings were consistent when solely doublet treatments, including apalutamide, enzalutamide, or darolutamide, were stratified according to disease volume. Overall survival (OS) data in ARANOTE are very immature, with up to date no significant differences in OS between the doublet regimen and the control group (hazard ratio: 0.81; 95% confidence interval: 0.59-1.12). The combination of darolutamide and ADT is likely-with the requirement of additional follow-up-to become another standard of care regimen for mHSPC following approval in the future. PATIENT SUMMARY: The phase 3 ARANOTE trial has shown the combination of darolutamide and androgen deprivation therapy (ADT) to have favorable outcomes for metastatic hormone-sensitive prostate cancer (mHSPC) over ADT monotherapy. This combination therapy was as effective as the already known doublet therapies for progression free-survival, and had a very favorable safety and toxicity profile. Following approval, the combination of darolutamide and ADT may become another standard of care regimen for mHSPC.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.034 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".