Aquablation for benign prostatic hyperplasia: real‐world prostate size relevance and bleeding events across 6 years
Bibliographic record
Abstract
OBJECTIVE: To present large-scale safety outcomes, with a particular focus on postoperative bleeding following Aquablation for benign prostatic hyperplasia. PATIENTS AND METHODS: Patients who underwent Aquablation between 2019 and 2024 across Asia, Europe, and North America were assessed to evaluate trends in treated prostate sizes, which were visualised using density plots. A corporate prospective database was maintained, incorporating case recordings and data collected by on-site company representatives. In addition, the incidence of postoperative bleeding-defined as transfusion or surgical takeback for haemostatic fulguration-was analysed using data from the United States Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database and procedure counts by the manufacturer. RESULTS: A total of 70 270 Aquablation procedures were evaluated over the period from 2019 to 2024. The mean (standard deviation) prostate volume was 87.3 (42.4) mL, with a maximum recorded size of 1189 mL. Density plot analysis of prostate volumes demonstrated consistent utilisation of Aquablation across the full range of prostate sizes throughout all years studied. The overall rate of blood transfusion or return to the operating room for haemostatic fulguration was 0.2%, indicating a favourable safety profile across a very wide range of prostate sizes. CONCLUSIONS: Aquablation has been consistently utilised across a broad spectrum of prostate sizes, with a low overall rate of transfusion or return to the operating room for bleeding control. These findings highlight the procedure's broad applicability and favourable safety profile in real-world practice from 2019 to 2024.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".