Real‐world use of androgen‐deprivation therapy intensification for metastatic hormone‐sensitive prostate cancer: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic literature review of real-world data (RWD) studies to summarise treatment patterns among men with metastatic hormone-sensitive prostate cancer (mHSPC). While androgen-deprivation therapy (ADT) is a primary treatment strategy for mHSPC, ADT intensification with androgen receptor pathway inhibitors (ARPIs) and/or chemotherapy is recommended by current guidelines and has improved clinical outcomes in the last decade. METHODS: We searched electronic databases (PubMed; Excerpta Medica dataBASE [EMBASE]) for eligible studies (retrospective or prospective observational RWD studies examining mHSPC treatment patterns) between database inception and July 2023, and manually screened the past 2 years of relevant conference proceedings. RESULTS: Of 2336 retrieved citations, 29 studies met the inclusion criteria, covering North America (United States, n = 21; Canada, n = 2), Europe (n = 8), and Asia (n = 6). Most studies utilised retrospective cohorts (n = 26) and included men with a median age of ≥70 years (n = 20). ADT monotherapy was predominantly used across geographies, followed by ADT + ARPI and ADT + docetaxel in the United States and Europe but not in Asia, where use of each combination remained low. Studies with recent electronic medical record data from cancer centres/registries showed >40% use of ADT + ARPI in the United States and Europe. Abiraterone was the most frequently used ARPI, followed by enzalutamide. Quantitative factors associated with ADT intensification were high disease burden, younger age, Eastern Cooperative Oncology Group performance status score of 0 to 1, fewer comorbidities, and oncologist physician specialty; qualitative factors were patient preference, unsatisfactory response to ADT, ability to tolerate adverse events, and absence of cost barriers. CONCLUSION: While there was an increasing trend in ADT intensification for mHSPC over the study period across geographies, use remained suboptimal considering the high proportion of patients who were still receiving ADT monotherapy only. These findings highlight the need for interventions to further optimise current mHSPC therapies with high guideline concordance.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".