Noninvasive magnetic resonance-guided focused ultrasound for tendon disruption: an <i>in vivo</i> Animal study
Bibliographic record
Abstract
Purpose Surgical resection of the tendon is an effective treatment for severe contracture. Magnetic Resonance-guided Focused Ultrasound (MRgFUS) is a non-invasive ultrasonic therapy which produces a focal increase in temperature, subsequent tissue ablation and disruption. We evaluated MRgFUS as a clinically translatable treatment modality to non-invasively disrupt in vivo porcine tendons.Material and methods In vivo Achilles tendons (n = 28) from 15–20kg Yorkshire pigs (n = 16) were randomly assigned to 4 treatment groups of 600, 900, 1200 and 1500 J. Pretreatment range of motion (ROM) of the ankle joint was measured with the animal under general anesthesia. Following MRgFUS treatment, success of tendon rupture, ROM increase, temperature, thermal dosage, skin burn, and histology analyses were performed.Results Rupture success was found to be 29%, 86%, 100% and 100% for treatment energies of 600, 900, 1200 and 1500 J respectfully. ROM difference at 90° flexion showed a statistically significant change in ROM between 900 J and 1200 J from 16° to 27°. There was no statistical significance between other groups, but there was an increase in ROM as more energy was delivered in the treatment. For each of the respective treatment groups, the maximal temperatures were 58.4 °C, 63.3 °C, 67.6 °C, and 69.9 °C. The average areas of thermal dose measured were 24.3mm2, 53.2mm2, 77.8mm2 and 91.6mm2. The average areas of skin necrosis were 5.4mm2, 21.8mm2, 37.2mm2, and 91.4mm2. Histologic analysis confirmed tissue ablation and structural collagen fiber disruption.Conclusions This study demonstrated that MRgFUS is able to disrupt porcine tendons in vivo without skin incisions.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".