AQUABEAM robotic system use‐results survey: Aquablation for the treatment of lower urinary tract symptoms due to benign prostatic hyperplasia in the Japanese Population
Bibliographic record
Abstract
OBJECTIVES: To evaluate the safety, efficacy, and patient-reported outcomes of Aquablation therapy using the AQUABEAM Robotic System for the treatment of lower urinary tract symptoms (LUTS) due to benign prostatic hyperplasia (BPH) in a Japanese population. METHODS: This post-market use-results survey included 103 Japanese men with BPH who underwent Aquablation across five centers with previously Aquablation naïve physicians. Data were collected at baseline, during the procedure, at discharge, and at 3 and 6 months post-procedure. Key outcomes included International Prostate Symptom Score (IPSS), quality of life (QoL), uroflowmetry parameters, and adverse events. RESULTS: The mean age of patients was 71.1 years, and the average prostate size was 82.3 mL. At 6 months, the mean IPSS significantly improved from 18.1 ± 9.0 to 6.1 ± 5.0 (p < 0.0001), and QoL scores improved from 4.9 ± 1.3 to 1.8 ± 1.3 (p < 0.0001). Uroflowmetry showed a significant increase in Qmax from 8.3 ± 4.4 to 15.5 ± 7.8 mL/s (p < 0.0001) and a decrease in post-void residual volume (PVR) from 85.6 ± 107.2 to 43.3 ± 60.0 mL (p = 0.0006). At the 30-day primary safety endpoint, there were no reported adverse events of pad-use incontinence, erectile dysfunction, or ejaculatory dysfunction reported, and no device-related serious adverse events were reported. One subject (0.97%) experienced a Clavien-Dindo grade 3 adverse event. CONCLUSIONS: Aquablation therapy using the AQUABEAM Robotic System is a reproducible, safe, and effective treatment for Japanese men with BPH, providing significant improvements in LUTS and QoL with a favorable safety profile.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".