Magnetic resonance-guided focused ultrasound for the treatment of tremor
Bibliographic record
Abstract
Magnetic resonance-guided focused ultrasound (MRgFUS) is an emerging treatment for tremor and other movement disorders. An incisionless therapy, it is becoming increasingly common worldwide. However, given MRgFUS’ relative novelty, there remain limited data on its benefits and adverse effects. We review the current state of evidence of MRgFUS for tremor, highlight its challenges, and discuss future perspectives. Essential tremor (ET) has been the major indication for MRgFUS since a milestone randomized controlled trial (RCT) in 2016, with substantial evidence attesting to the efficacy and acceptable safety profile of this treatment. Patients with other tremor etiologies are also being treated with MRgFUS, with studies – including an RCT – suggesting parkinsonian tremor in particular responds well to this intervention. Additionally, targets other than the ventral intermediate nucleus, such as the subthalamic nucleus and internal segment of the globus pallidus, have been reported to improve parkinsonian symptoms beyond tremor, including rigidity and bradykinesia. Although MRgFUS is encumbered by certain unique technical challenges, it nevertheless offers significant advantages compared to alternative neurosurgical interventions for tremor. The fast-growing interest in this treatment modality will likely lead to further scientific and technological advancements that could optimize and expand its therapeutic potential.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.024 |
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".