Image-Guided High Intensity Focused Ultrasound System for Large Animal Nerve Ablation Studies
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) is a form of thermal ablation technique, which can treat a variety of medical afflictions. One promising therapeutic use is the permanent destruction of nerves non-invasively in patients with severe spasticity or certain types of pain (e.g., phantom limb pain). To this end, HIFU requires ultrasound guidance, which allows the non-invasive, target-specific deposition of thermal energy to the targeted nerve, thereby blocking axonal conduction. In this paper, a composite system comprising both ultrasound-imaging and HIFU therapy was developed and used to induce localized non-invasive nerve blockage in an in vivo large animal study. Five pigs were used with the femoral nerve as the target. Calibrated needle thermocouples inserted at the target site were employed to monitor the target tissue temperature. The degree of nerve blockage was assessed by measuring compound action potential (CAP) signal with a clinical nerve electrophysiology system before and after HIFU exposures. An average CAP signal amplitude reduction of 49% of baseline with a standard deviation of 9% was observed after 20-30 min post exposure. These results demonstrate the feasibility of the proposed ultrasound-guided HIFU modality as a potential non-invasive nerve ablation method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".