Noninvasive release of tendons using MRI guided focused ultrasound: a hybrid therapy using long-pulse focused ultrasound followed by thermal ablation
Bibliographic record
Abstract
Introduction Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thermal ablation is an effective noninvasive ultrasonic therapy to disrupt in vivo porcine tendon but is prone to inducing skin burns. We evaluated the safety profile of a novel hybrid protocol that minimizes thermal spread by combining long-pulse focused ultrasound followed by thermal ablation.Methods In-vivo Achilles tendons (hybrid N = 15, thermal ablation alone N = 21) from 15 to 20 kg Yorkshire pigs were randomly assigned to 6 treatment groups in two studies. The first (N = 21) was ablation (600, 900, or 1200 J). The second (N = 15) was hybrid: pulsed FUS (13.5 MPa peak negative pressure) followed by ablation (600, 900, or 1200 J). Measurements of ankle range of motion, tendon temperature, thermal dose (240 CEM43), and assessment of skin burn were performed in both groups.Results Rupture was comparable between the two protocols: 1/5 (20%), 5/5 (100%) and 5/5 (100%) for hybrid protocol, compared to 2/7 (29%), 6/7 (86%) and 7/7 (100%) for the ablation-only protocol with energies of 600, 900, and 1200 J, respectively. The hybrid protocol produced lower maximum temperatures, smaller areas of thermal dose, fewer thermal injuries to the skin, and fewer full-thickness skin burns. The standard deviation for the area of thermal injury was also smaller for the hybrid protocol, suggesting greater predictability.Conclusion This study demonstrated a hybrid MRgFUS protocol combining long-pulse FUS followed by thermal ablation to be noninferior and safer than an ablation-only protocol for extracorporeal in-vivo tendon rupture for future clinical application for noninvasive release of contracted tendon.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".