Volumetric feedback ablation of uterine fibroids using magnetic resonance-guided high intensity focused ultrasound therapy
Bibliographic record
Abstract
OBJECTIVE: The purpose of this prospective multicenter study was to assess the safety and technical feasibility of volumetric Magnetic Resonance-guided High Intensity Focused Ultrasound (MR-HIFU) ablation for treatment of patients with symptomatic uterine fibroids. METHODS: Thirty-three patients with 36 fibroids were treated with volumetric MR-HIFU ablation. Treatment capability and technical feasibility were assessed by comparison of the Non-Perfused Volumes (NPVs) with MR thermal dose predicted treatment volumes. Safety was determined by evaluation of complications or adverse events and unintended lesions. Secondary endpoints were pain and discomfort scores, recovery time and length of hospital stay. RESULTS: The mean NPV calculated as a percentage of the total fibroid volume was 21.7%. Correlation between the predicted treatment volumes and NPVs was found to be very strong, with a correlation coefficient r of 0.87. All patients tolerated the treatment well and were treated on an outpatient basis. No serious adverse events were reported and recovery time to normal activities was 2.3 ± 1.8 days. CONCLUSION: This prospective multicenter study proved that volumetric MR-HIFU is safe and technically feasible for the treatment of symptomatic uterine fibroids. KEY POINTS: • Magnetic-resonance-guided high intensity focused ultrasound allows non-invasive treatment of uterine fibroids. • Volumetric feedback ablation is a novel technology that allows larger treatment volumes • MR-guided ultrasound ablation of uterine fibroids appears safe using volumetric feedback.
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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) | 0.000 | 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) | 0.001 | 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 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".