MR-guided focused ultrasound thalamotomy in a patient with thrombocytopenia: illustrative case
Bibliographic record
Abstract
BACKGROUND: MR-guided focused ultrasound (MRgFUS) thalamotomy is a relatively novel technique used to treat essential tremor (ET) and tremor-dominant Parkinson's disease. It is thought to be a safer alternative to other treatments, with no reported cases of major intracranial bleeding to date. However, experience with patients at increased risk of hemorrhage is still limited. OBSERVATIONS: The authors present the case of a successfully treated ET patient with thrombocytopenia who experienced no postprocedural complications, detailing the surgical optimization strategy used. They conducted a systematic review of the literature, focusing on articles addressing MRgFUS in patients with known coagulation disorders. The Embase, Scopus, and MEDLINE databases were queried from their inception to August 20, 2024. Of the 973 screened abstracts, 2 relevant studies were identified. The first study included 40 patients undergoing MRgFUS without interruption of antiplatelet or anticoagulant therapy, while the second was a case report of a patient with von Willebrand disease. Both studies reported satisfactory safety profiles, with no increased incidence of hemorrhagic or other complications. LESSONS: Although evidence is limited due to the small number of studies and cases, these findings suggest that MRgFUS, with appropriate periprocedural optimization, represents a viable option for patients at heightened risk of intracranial hemorrhage. https://thejns.org/doi/10.3171/CASE2525.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".