Rezūm water vapor thermal therapy for large-volume (≥80 mL), symptomatic, benign prostatic enlargement
Bibliographic record
Abstract
INTRODUCTION: Water vapor thermal therapy (WVTT), Rezūm, is a minimally invasive therapy that uses water vapor to ablate benign prostatic tissue. This study aimed to present the prospective, multicenter outcomes of the largest cohort of prostates ≥80 mL treated with Rezūm. METHODS: This study involved a prospective, WVTT registry that collated information from two high-volume centers between April 2019 and August 2024. Baseline medical histories, uroflowmetry (peak flow rate [Qmax], postvoid residual [PVR], and validated questionnaires (International Prostate Symptom Score [IPSS], IPSS quality of life (QoL), Benign Prostatic Hyperplasia Impact Index [BPHII], International Index of Erectile Function [IIEF-15], Male Sexual Health Questionnaire for Ejaculatory Dysfunction [MSHQ-EjD]) were recorded. The main outcomes assessed included symptom scores, functional improvement, and safety at baseline, six, 12, and 24 months. RESULTS: A total of 259 patients with a prostate volume ≥80 mL were treated with Rezūm. The median prostate volume was 105 mL, with 207 patients (81.2%) exhibiting a median lobe. The IPSS improved from 21.8 at baseline to 5.7 at 24 months. The IPSS QoL score improved from 4.5 at baseline to 1.1 at 24 months. At baseline, the Qmax rate was 8.2 mL/s, increasing to 14.9 mL/s at 24 months. PVR volume decreased from 132.5 mL at baseline to 90 mL at 24 months. The BPHII decreased from 7.5 at baseline to 2.3 at 24 months. There was no significant change in sexual function as measured by IIEF and MSHQ. CONCLUSIONS: Rezūm therapy is a safe, effective, and minimally invasive option for managing large prostates (≥80 mL), providing significant and sustained improvements in urinary symptoms with minimal impact on sexual function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".