Removal of Prostate Calcifications Prior to TULSA-PRO With the Aid of Real-Time Ultrasound Imaging: Our Technique and Experience
Bibliographic record
Abstract
Prostate calcifications can impede ultrasound energy propagation during transurethral ultrasound ablation (TULSA-PRO, Profound Medical Inc., Mississauga, ON, Canada). We instituted a pre-procedure screening protocol using low-dose pelvic computed tomography (CT) with ≤2-mm slices to detect calcifications larger than 3 mm along the planned ablation path; when obstructing deposits are present, we clear them immediately before treatment. Drawing on image-guided principles used in Aquablation (AquaBeam Robotic System, PROCEPT BioRobotics, San Jose, CA, USA), we correlate preoperative sagittal CT with real-time, stepper-mounted transrectal ultrasound (TRUS) to localize targeted prostate tissue for limited bipolar resection, while continuously visualizing the loop position on TRUS. In eight consecutive candidates with obstructive calcifications ≥3 mm, post-procedure low-dose CT confirmed clearance in every case, enabling uninterrupted continuation to the TULSA-PRO workflow. This streamlined, CT-referenced, TRUS-guided technique offers a practical pathway to preserve beam-path integrity without broad tissue debulking. The dataset of eight consecutive candidates with obstructive calcifications ≥3 mm reported here is original and has not been previously published in whole or in part.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".