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Record W4414714648 · doi:10.2147/rru.s510367

Management and Treatment of Benign Prostatic Hyperplasia Symptoms: Current Insights

2025· review· en· W4414714648 on OpenAlexaff
Othmane Zekraoui, Naeem Bhojani, Kevin C. Zorn, Dean Elterman, Bilal Chughtai

Bibliographic record

VenueResearch and Reports in Urology · 2025
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of TorontoRoyal Victoria HospitalUniversité de Montréal
Fundersnot available
KeywordsLower urinary tract symptomsHyperplasiaBenign prostatic hyperplasia (BPH)Quality of life (healthcare)ProstatePatient careProstate diseaseUrinary retentionMedical therapy

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.090
GPT teacher head0.451
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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