Systemic treatment of metastatic castration-sensitive prostate cancer: A meta-analysis of efficacy and safety
Bibliographic record
Abstract
BACKGROUND: Within the last 10 years, multiple clinical randomized controlled trials in metastatic castration-sensitive prostate cancer patients (mCSPC) have demonstrated that androgen deprivation therapy (ADT) combination therapy is significantly superior to ADT alone. However, there are no guidelines that recommend the best clinical decisions with regard to multi-selective systemic therapy for mCSPC. METHODS: Two independent researchers have conducted a systematic literature search of the Cochrane Central, Web of Science, Clinical Trials. gov and EU Clinical Trial Register databases with a search deadline of July 5, 2022. Hazard Ratio (HR) and 95% confidence intervals were analyzed and extracted for overall and progression-free survival and their subgroups, as well as the number of adverse events. Trial quality was assessed through the Risk of Bias Form and the Newcastle-Ottawa Scale, and systematic reviews and meta-analyses were carried out in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) project. RESULTS: 8704 patients from 7 studies participated in trials comparing the effectiveness of 4 ADT combination treatments (including ADT + docetaxel, ADT + abiraterone acetate, ADT + enzalutamide, ADT + apalutamide) and ADT alone (ADT + placebo/no-treatment). The results suggested that ADT combination therapy was significantly better than ADT alone. Furthermore, based on the patient's initial clinical characteristics and expected prognosis, we tentatively provided relevant optimal treatment options. CONCLUSIONS: ADT + ARAT outperformed ADT ± docetaxel in OS/PFS for mCSPC, with strategies tailored to patient profiles. Future work will leverage advanced analytics and standardized reporting; direct combination trials are critical for precise guidance.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.066 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".