Two‐year follow‐up results of magnetic resonance imaging‐guided focused ultrasound unilateral pallidotomy for Parkinson’s disease
Bibliographic record
Abstract
Abstract Background Posteroventral globus pallidus internus (GPi) pallidotomy is one of the therapeutic options for motor fluctuations in Parkinson's disease (PD). Transcranial magnetic resonance imaging‐guided focused ultrasound (MRgFUS) is a new intervention to ablate an intracranial target. Aim To investigate the long‐term efficacy and safety of MRgFUS unilateral GPi pallidotomy for PD. Methods This was a prospective and open‐labeled study involving a single center. We enrolled 3 PD patients with medication‐refractory motor fluctuations (3 women, aged 59 to 78 years). Participants underwent MRgFUS unilateral GPi pallidotomy and were evaluated serially for 2 years using the Unified Parkinson's Disease Rating Scale (UPDRS) and Unified Dyskinesia Rating Scale (UDysRS). Additionally, we assessed safety issues during the study period. Results Although motor fluctuations improved in 2 patients, the motor function in the off‐medication state and levodopa‐induced dyskinesia (LID) exacerbated in 1 of them. In the other patient, LID improved for 2 years; however, improvement of the motor function was limited and it exacerbated. Patients developed neither serious nor delayed complications. Conclusion The efficacy of MRgFUS unilateral GPi pallidotomy differed in each patient and might depend on the natural course of PD. No safety issues were observed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".