Patient-reported Outcomes in KEYNOTE-921: Pembrolizumab with Docetaxel and Prednisone for Patients with Metastatic Castration-resistant Prostate Cancer
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In the phase 3 KEYNOTE-921 study, the combination of pembrolizumab with docetaxel did not improve efficacy outcomes significantly in patients with chemotherapy-naive metastatic castration-resistant prostate cancer (mCRPC) after next-generation hormonal agents (NHAs). This analysis of KEYNOTE-921 assessed patient-reported outcomes (PROs) associated with pembrolizumab plus chemotherapy versus chemotherapy alone. METHODS: Eligible patients were assigned randomly in a 1:1 ratio to receive pembrolizumab or placebo, both with docetaxel and prednisone. The time to pain progression measured by the Brief Pain Inventory-Short Form was a secondary end point. Health-related quality of life (HRQoL) scores, including disease-related symptoms, assessed by the Functional Assessment of Cancer Therapy-Prostate and EuroQol five-dimension five-level were prespecified exploratory end points. KEY FINDINGS AND LIMITATIONS: The PRO analysis set comprised 1028 patients; the median follow-up was 22.7 mo from baseline until database cutoff (June 20, 2022). The median time to pain progression was 21.1 mo (95% confidence interval [CI] 13.7 to not reached [NR]) and NR (95% CI 13.8-NR) in the pembrolizumab and placebo arms, respectively (hazard ratio 1.05; 95% CI 0.77-1.43). Changes from baseline in PRO end points were similar between treatment arms and remained generally stable throughout the study. Limitations include a lack of formal hypothesis testing to detect between-arm differences in PROs. CONCLUSIONS AND CLINICAL IMPLICATIONS: No meaningful differences in PROs were observed in patients with mCRPC treated with pembrolizumab plus chemotherapy versus chemotherapy alone. These findings indicate that adding an immune checkpoint inhibitor to chemotherapy does not positively or negatively impact HRQol in patients with mCRPC previously treated with NHAs.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".