Focused Ultrasound Pallidothalamic Tractotomy in Cervical Dystonia: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: No clinical trials have been reported on the use of focused ultrasound (FUS) for treating cervical dystonia. OBJECTIVE: We aimed to confirm the efficacy and safety of FUS pallidothalamic tractotomy for cervical dystonia. METHODS: This was a prospective, open-label, non-controlled pilot study. The primary outcome was defined as a change in the score for the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) from baseline to 6 months after FUS pallidothalamic tractotomy. The secondary outcomes included a change in the neck scale for the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS), mood scales including Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Apathy Evaluating Scale (AES), and adverse events. Patients were assessed for TWSTRS, BFMDRS, and adverse events at baseline, 1 week, 1 month, 3 months, and 6 months after treatment. BDI, BAI, and AES were assessed at baseline and 6 months after treatment. RESULTS: Ten patients were enrolled in this study. The mean age of onset of dystonia was 51.6 ± 10.2 years. The TWSTRS at 6 months (29.9 ± 16.0, range: 3-55) was significantly improved by 43.4% (P < 0.001) from baseline. The BFMDRS-Neck scales at 6 months (4.2 ± 2.8) were significantly improved by 38.2% (P < 0.001) from baseline. The BDI, BAI, and AES at 6 months were improved by 23.2%, 10.9%, and 30.3%, respectively from baseline. Reduced hand dexterity in three patients and weight gain in two patients were confirmed at the last evaluation. CONCLUSION: This study suggests that FUS pallidothalamic tractotomy may be an effective treatment option for patients with cervical dystonia. © 2024 International Parkinson and Movement Disorder Society.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".