Evolving Treatment Paradigms in Metastatic Hormone-Sensitive Prostate Cancer: Expert Narrative Review
Bibliographic record
Abstract
The treatment landscape of metastatic hormone-sensitive prostate cancer (mHSPC) has transformed significantly with the advent of triplet therapy involving androgen deprivation therapy (ADT), docetaxel, and androgen receptor signalling inhibitors (ARSIs). While clinical guidelines increasingly support early intensification, real-world practice remains challenged by patient heterogeneity, evolving evidence, and limited consensus on treatment sequencing. This narrative review integrates evidence from landmark trials, clinical guidelines, and expert insights from oncologists managing mHSPC in India. Findings affirm that triplet therapy, particularly with darolutamide, improves survival in high-volume disease and underscores the need for personalized treatment based on disease burden, comorbidities, and genomic profiles. The review also highlights gaps in real-world data, sequencing strategies, and biomarker-driven therapy, reinforcing the need for precision medicine and locally relevant evidence to guide treatment. Ultimately, optimizing mHSPC management requires harmonizing guideline-based approaches with individualized, real-world decision making to improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".