An analysis of health-related quality of life in the phase III PROSELICA and FIRSTANA studies assessing cabazitaxel in patients with metastatic castration-resistant prostate cancer
Bibliographic record
Abstract
BACKGROUND: Men with metastatic castration-resistant prostate cancer (mCRPC) are living longer, therefore optimizing health-related quality of life (HRQL), as well as survival outcomes, is important for optimal patient care. The aim of this study was to assess the HRQL in patients with mCRPC receiving docetaxel or cabazitaxel. PATIENTS AND METHODS: (D75) in patients with chemotherapy-naive mCRPC. HRQL and pain were analyzed using protocol-defined, prospectively collected, Functional Assessment of Cancer Therapy-Prostate (FACT-P) and McGill-Melzack questionnaires. Analyses included definitive improvements in HRQL, maintained or improved HRQL, and HRQL over time. RESULTS: In total, 2131 patients were evaluable for HRQL across the two studies. In PROSELICA, 38.8% and 40.5% of patients receiving C20 and C25, respectively, had definitive FACT-P total score (TS) improvements. In FIRSTANA, 43.4%, 49.7%, and 44.9% of patients receiving D75, C20, and C25, respectively, had definitive FACT-P TS improvements. In both trials, definitive improvements started after cycle 1 and were maintained for the majority of subsequent treatment cycles. More than two-thirds of patients maintained or improved their FACT-P TS. CONCLUSIONS: In PROSELICA and FIRSTANA, >40% of the 2131 evaluable patients with mCRPC had definitive FACT-P TS improvements; improvements occurred early and were maintained. More than 75% of patients maintained or improved their FACT-P TS.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".