Management and Treatment of Benign Prostatic Hyperplasia Symptoms: Current Insights
Bibliographic record
Abstract
Othmane Zekraoui,1 Naeem Bhojani,1,2 Kevin C Zorn,3 Dean Elterman,4 Bilal Chughtai5,6 1Faculty of Medicine, Université de Montréal, Montreal, QC, Canada; 2Division of Urology, Department of Surgery, Université de Montréal Health Center, Montreal, QC, Canada; 3BPH Canada Prostate Surgical Institute, Mont-Royal Surgical Center, Montreal, QC, Canada; 4Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; 5Department of Urology, Northwell Health, Plainview, NY, USA; 6Department of Urology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Hempstead, NY, USACorrespondence: Bilal Chughtai, Northwell Health, 8 Greenfield Road, Syosset, NY, 11791, USA, Email Bchughtai@northwell.eduAbstract: Benign prostatic hyperplasia (BPH) is a common cause of lower urinary tract symptoms (LUTS) in aging men that can significantly impair their quality of life. This review provides updated insights into this condition’s pathophysiology, diagnostic strategies, and the full spectrum of management approaches, including lifestyle changes, medical management, minimally invasive surgical therapies, and traditional surgical interventions. For each modality, we sought to provide an overview of the mechanism of action, efficacy, safety, as well as guideline-backed patient selection recommendations issued by three major BPH guidelines. With the various existing management strategies, the role of shared decision making is emphasized to align the therapeutic choices with symptom severity, prostate anatomy, comorbidities, but also with individual patient values. By summarizing the current and most updated literature on BPH management and treatment, our goal is to support evidence-based clinical decisions and improve care and outcomes for men suffering from BPH-related LUTS.Keywords: prostatic hyperplasia, lower urinary tract symptoms, male, quality of life
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".