A drug safety evaluation of enzalutamide to treat advanced prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer (PC) is the most common cancer in North American men. Advanced PC is incurable. The androgen receptor antagonist enzalutamide is used to manage advanced PC, often over a period of months or years; it is therefore important to evaluate the safety profile of enzalutamide. AREAS COVERED: This literature review presents safety data from pivotal trials and real-world data studies of enzalutamide in patients with advanced PC, including metastatic hormone-sensitive prostate cancer (mHSPC), nonmetastatic castration-resistant prostate cancer (nmCRPC), and metastatic castration-resistant prostate cancer (mCRPC). A large body of evidence supports the maintenance or improvement in the health-related quality of life (HRQoL) afforded by enzalutamide treatment in patients with mHSPC, nmCRPC, or chemotherapy-naïve mCRPC, as well as improvement in the HRQoL in patients with later-stage symptomatic mCRPC. Efficacy data from clinical trials are also briefly discussed. EXPERT OPINION: We aim to provide clinicians with a better understanding of how to properly interpret enzalutamide clinical trial safety data. This knowledge may help clinicians guide their patients with PC to achieve optimal clinical benefit from enzalutamide therapy, and to properly manage their patients to mitigate any potential risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".