Addition of Olaparib to the New Hormonal Agent Regimen for Metastatic Castration-Resistant Prostate Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The emergence of olaparib, a poly (adenosine diphosphate (ADP)-ribose) polymerase (PARP) inhibitor to treat metastatic castration-resistant prostate cancer (mCRPC), created a measurable clinical question on whether the agent positively influences the treatment outcomes and acceptable safety factors. The objective was to elaborate on the efficacy and safety of olaparib-added regimens in treating mCRPC patients as compared to the established guideline. Methods: The literature search was performed on several scientific databases, e.g., PubMed, Cochrane, and ScienceDirect, by applying the Boolean Term method. Statistical and risk of bias (RoB) analyses were calculated through RevMan 5.4.1. to investigate our outcomes, i.e., progression-free survival (PFS) and overall survival (OS) with the reported adverse effects (AEs). These outcomes were presented in hazard ratio (HR) and risk ratio (RR). Results: Three trials consisting of 1,325 individuals with comparable baseline characteristics were investigated. The meta-analysis showed that introducing olaparib into the regimens significantly improved the PFS (HR 0.59 (0.48 - 0.73); P < 0.05), which disclosed even better outcomes among mutated homologous recombinant repair (HRR) and ataxia-telangiectasia mutated (ATM) gene (HR 0.43 (0.30 - 0.62); P < 0.05) in 95% confidence interval (CI). Furthermore, similar outcomes were observed in OS analysis (HR 0.81 (0.67 - 0.99); P < 0.05), despite olaparib group disclosed higher AEs rate with insignificant difference in mortality rate. Conclusion: -mutated individuals.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".