Magnetic resonance–guided focused ultrasound thalamotomy for treatment of essential tremor: A 2‐year outcome study
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance-guided focused ultrasound is an emerging, minimally invasive thermoablation technique for medically refractory essential tremor. Beyond the initial year, data regarding efficacy and potential predictors of efficacy are still preliminary. OBJECTIVES: The objective of this study was to assess the outcome at 2 years and the association between lesion volume and outcome 1 year after treatment. METHODS: We reviewed data from 37 patients who underwent unilateral magnetic resonance-guided focused ultrasound thalamotomy, with primary outcome being dominant tremor subscore of the Clinical Rating Scale for Tremor. We used multivariable linear regression to model initial lesion volume with 1-year outcome, adjusting for other clinically relevant variables. RESULTS: Although we detected a trend in loss of clinical benefit within the first year, the dominant tremor score at 2 years continued to be significantly improved (43.4%, 95% confidence interval 27.8%-59.0%) from baseline. Secondarily, initial lesion volume is significantly associated with 1-year outcome. CONCLUSION: Our findings show that magnetic resonance-guided focused ultrasound thalamotomy results in sustained tremor reduction for medically refractory essential tremor even in the long term, and we highlight areas for improvement.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".