Transfusion rates after 800 Aquablation procedures using various haemostasis methods
Bibliographic record
Abstract
OBJECTIVE: To determine if athermal methods are as effective in preventing blood transfusions as the use of cautery across various prostate volumes following prostate tissue resection for benign prostatic hyperplasia using Aquablation. PATIENTS AND METHODS: The current commercial AQUABEAM robot that performs Aquablation therapy was first used in 2014. Since then numerous clinical studies have been conducted in various countries; Australia, Canada, Germany, India, Lebanon, Spain, New Zealand, United Kingdom, and the United States. All of the clinical trial data since 2014 were pooled with the early commercial procedures from France, Germany, and Spain to determine the effectiveness of haemostatic techniques in reducing the transfusion rate in patients after Aquablation. RESULTS: In all, 801 patients were treated with Aquablation therapy from 2014 to early 2019. The mean (SD, range) prostate volume was 67 (33, 20-280) mL and 31 (3.9%) transfusions were reported. The largest contributing factor to transfusion risk was prostate size and method of traction. There was an increasing risk of transfusions in larger prostates when robust traction using a catheter-tensioning device (CTD) without cautery was used, ranging from 0.8% to 7.8% in prostates ranging from 20 to 280 mL. However, when standard traction (taping the catheter to the leg, gauze knot synched up to the meatus, or no traction at all) was used and where the surgeon performed bladder neck cautery only when necessary, the risk of transfusion was 1.4-2.5% in prostates ranging from 20 to 280 mL. CONCLUSIONS: While the athermal subgroup with robust traction with a CTD had comparable transfusion rates for smaller prostates, the risk increased significantly as prostate volume increased. With standard traction methods and selective bladder neck cautery, the risk of transfusion was reduced to 1.9% across all prostate sizes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".