Localised prostate cancer treated with MRI-guided transurethral ultrasound ablation: phase I trial results
Bibliographic record
Abstract
Enrolled were 30 patients with biopsy-proven, low-risk prostate cancer (age ≥ 65y, T1c/T2a, PSA ≤ 10ng/ml, Gleason 6 (3+3)). Whole-gland prostate ablation was performed with MR-TULSA using the PAD-105 (Profound Medical Inc., Canada) and a 3T MRI (Siemens, Germany) in one single treatment session under general anaesthesia and 3D active MR-thermometry feedback control. Contrast-enhanced MRI (CE-MRI) immediately following the ablation and at 12 months confirmed thermal coagulation. There were no intraoperative complications with normal micturition resuming after catheter removal. Median (range) treatment time and prostate volume were 36 (24–61) min and 44 (21– 95) ml, respectively. Maximum temperature during treatment depicted a continuous region of heating shaped accurately to the prostate within 0.1 ± 1.3 mm, with average over- and under- targeted volumes of 0.8 and 1.0 ml, respectively. Regions of acute cell kill on CE-MRI correlated well with treated volume on MR-thermometry. Successful treatment was further confirmed by a median PSA decrease from 5.35 to 0.70 ng/ml at 1 month (n=29), remaining stable to 0.65 ng/ml at 6 months (n=16). Phase I results show that MR-TULSA represents a minimally-invasive treatment option for safe, effective and accurate whole-gland thermal ablation of organ-confined prostate cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".