Bibliographic record
Abstract
INTRODUCTION: Magnetic resonance-guided focused ultrasound (MRgFUS) has emerged as a leading noninvasive therapy for tremor, offering a precise, lesion-based alternative to deep brain stimulation (DBS) and traditional lesioning techniques. By using phased ultrasound arrays to focus energy at intracranial targets, MRgFUS allows for real-time visualization and monitoring, improving safety and efficacy. Initially developed for essential tremor (ET), MRgFUS-VIM-thalamotomy has gained widespread acceptance and is now a first-line option for tremor-dominant Parkinson's disease (TDPD) and other tremor syndromes. AREAS COVERED: This review discusses the fundamental physics of focused ultrasound, key anatomical targets, and the clinical application of MRgFUS thalamotomy, pallidotomy, and subthalamotomy. Skull density ratio (SDR) and energy efficiency are highlighted as crucial factors affecting treatment outcomes. The evolution of MRgFUS as a bilateral treatment, along with the exploration of novel targets such as the pallidothalamic tract, is examined. Additionally, we discuss advancements in FUS neuromodulation, which could complement lesioning by providing temporary or reversible symptom relief. EXPERT OPINION: MRgFUS is poised to further revolutionize tremor treatment with frameless technology, staged bilateral procedures, and the integration of neuromodulation. Future developments may allow for precise, adaptive therapies that enhance both efficacy and patient experience.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".