Systemic therapies for metastatic hormone‐sensitive prostate cancer: network meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: To perform a systematic review and network meta-analysis to compare the efficacy and safety of currently available treatments for the management of metastatic hormone-sensitive prostate cancer (mHSPC), as there has been a paradigm shift with the use of next-generation androgen receptor inhibitors (ARIs) and docetaxel. METHODS: Multiple databases were searched for articles published before May 2020 according to the Preferred Reporting Items for Systematic Review and Meta-analysis extension statement for network meta-analysis. Studies comparing overall/progression-free survival (OS/PFS) and/or adverse events (AEs) in patients with mHSPC were eligible. RESULTS: Nine studies (N = 9960) were selected, and formal network meta-analyses were conducted. Abiraterone (hazard ratio [HR] 0.83, 95% credible interval [CrI] 0.76-0.90), docetaxel (HR 0.90, 95% CrI 0.82-0.98), and enzalutamide (HR 0.85, 95% CrI 0.73-0.99) were associated with significantly better OS than androgen-deprivation therapy (ADT), and abiraterone emerged as the best option. Abiraterone (HR 0.71, 95% CrI 0.67-0.76), apalutamide (HR 0.73, 95% CrI 0.65-0.81), docetaxel (HR 0.84, 95% CrI 0.78-0.90), and enzalutamide (HR 0.67, 95% CrI 0.63-0.71) were associated with significantly better PFS than ADT, and enzalutamide emerged as the best option. Abiraterone (HR 0.85, 95% CrI 0.78-0.93), apalutamide (HR 0.87, 95% CrI 0.77-0.98), and enzalutamide (HR 0.80, 95% CrI 0.73-0.88) were significantly more effective than docetaxel. Regarding AEs, apalutamide was the likely best option among the three ARIs. In patients with low-volume mHSPC, enzalutamide was the best option in terms of OS and PFS. CONCLUSIONS: All three ARIs are effective therapies for mHSPC; apalutamide was the best tolerated. All three seemed more effective than docetaxel. These findings may facilitate individualised treatment strategies and inform future comparative trials.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.055 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".