Men with lower urinary tract symptoms secondary to BPH undergoing Aquablation with very large prostates (> 150 mL).
Bibliographic record
Abstract
INTRODUCTION: The AUA guidelines for benign prostatic hyperplasia distinguish treatments based upon prostate volume (PV), particularly for very large prostates (> 150 mL). While the clinical outcomes and benefits of Aquablation have been studied for men with average and large prostates, it is unknown whether this technology can be used for very large prostates. MATERIALS AND METHODS: Men with PV > 150 mL undergoing Aquablation were identified retrospectively from four North American hospitals. The surgical times and clinical outcomes of men with very large prostates (> 150 mL) were compared to data from men with average PV ≤ 80 mL (WATER study) and large PV 80 mL-150 mL (WATER II study). RESULTS: The average PV of men who underwent Aquablation with very large prostates was 209 mL ± 56 (n = 34, range 151-362 mL), large PV 107 mL ± 20 (n = 101, range 80-150 mL) and average PV 54 mL ± 16 (n = 116, range 30-80 mL). For men with PV > 150 mL, baseline IPSS was 19 ± 6. With a mean follow up of 7 ± 9 months, the IPSS improved to 7 ± 5 (p < 0.001). Peak urinary flow rate, Qmax, improved from 7 ± 4 mL/s to 19 ± 5 mL/s (p<0.001). Compared to the two other PV groups, there were no differences in terms of improvements in IPSS, quality of life, or uroflowmetry. There were no reports of transfusions (0%) in the cohort of men with very large prostates. CONCLUSIONS: In the present study, we demonstrate that Aquablation is effective and safe in prostates greater than 150 mL while showing consistent outcomes compared to average and large prostates sizes.
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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 | 0.000 | 0.000 |
| 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".