Emerging drugs for the treatment of benign prostatic hyperplasia: a 2023 update
Bibliographic record
Abstract
INTRODUCTION: Benign prostatic hyperplasia (BPH) is a condition that affects over 50% of men as they enter their fifth decade of life, often leading to lower urinary tract symptoms (LUTS). Primary treatment options include alpha blockers, 5-alpha reductase inhibitors, and phosphodiesterase-5 inhibitors. However, these medications can have some side effects, and there is a noticeable dearth of information addressing the long-term use of these medications. Thus, the exploration of all treatment modalities helps ensure patients receive personalized and effective care. Consequently, the primary objective of this review is to identify potential emerging medications for the treatment of BPH. AREAS COVERED: We conducted an extensive review of articles discussing pharmacotherapy for BPH spanning the last 15 years. Our information gathering process involved Scopus, PubMed-MEDLINE, Cochrane, Wiley Online Library Google Scholar, ClinicalTrials.gov, and the PharmaProjects database. This approach ensures that readers gain an in-depth knowledge of the existing therapeutic agents as well as promising avenues for managing BPH. EXPERT OPINION: BPH treatment targets a patient's specific constellation of symptoms. Therefore, a broad knowledge base encompassing various treatment options is paramount in ensuring optimal treatment. Looking forward, the emphasis on personalization promises to reshape the landscape of BPH treatment and improve patient outcomes.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".