Aquablation therapy in large prostates (80–150 cc) for lower urinary tract symptoms due to benign prostatic hyperplasia: WATER II 3‐year trial results
Bibliographic record
Abstract
Objective: The objective of this study is to determine if Aquablation therapy can maintain its effectiveness in treating men with lower urinary tract symptoms (LUTS) due to benign prostatic hyperplasia (BPH) with large-volume (80-150 cc) prostates at 3 years. Subjects and Methods: One hundred one men with moderate-to-severe BPH symptoms and prostate volumes between 80 and 150 cc were enrolled in a prospective, nonrandomized, multicenter, international clinical trial in late 2017. Baseline, procedural, and follow-up parameters were recorded at baseline and scheduled postoperative visits. IPSS, Qmax, and treatment failure are reported at 3 years. Results: < 0.0001). Maximum urinary flow increased from 8.7 to 18.5 cc/s. At 3 year follow-up, 6% of treated patients needed BPH medication and an additional 3% required surgical retreatment for LUTS. Conclusions: Three-year follow-up demonstrates a sustained symptom reduction response along with low irreversible complications to Aquablation in men with LUTS due to BPH and prostates of 80-150 cc. Current treatment options available for men with prostates of this size have similar efficacy outcomes but are burdened with high rates of irreversible complications. There are now numerous clinical studies with Aquablation used in various prostates sizes, and it should be offered as an option to men with LUTS due to BPH.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".