Improvement of Non-motor Symptoms and Quality of Life After Deep Brain Stimulation for Refractory Dystonia: A 1-Year Follow-Up
Bibliographic record
Abstract
Introduction: Deep brain stimulation (DBS) is a treatment option for refractory dystonia's motor symptoms, while its non-motor symptoms (NMS) have been less systematically assessed. We aimed to describe the effects of DBS on NMS in refractory generalized inherited/idiopathic dystonia prospectively. Methods: We evaluated patients before and 1 year after DBS surgery and applied the following scales: Burke–Fahn–Marsden Rating Scale (BFMRS), NMS Scale for Parkinson's Disease (NMSS-PD), Parkinson's Disease Questionnaire-8, short-form Brief Pain Inventory (BPI), Neuropathic Pain Symptom Inventory (NPSI), and short-form McGill Pain Questionnaire (MPQ). Results: Eleven patients (38.35 ± 11.30 years) underwent surgery, all with generalized dystonia. Motor BFMRS subscore was 64.36 ± 22.94 at baseline and 33.55 ± 17.44 1 year after DBS surgery (47.9% improvement, p = 0.003). NMSS-PD had a significant change 12 months after DBS, from 70.91 ± 59.07 to 37.18 ± 55.05 (47.5% improvement, p = 0.013). NMS changes were mainly driven by changes in the gastrointestinal (p = 0.041) and miscellaneous domains (p = 0.012). Seven patients reported chronic pain before DBS and four after it. BPI's severity and interference scores were 4.61 ± 2.84 and 4.12 ± 2.67, respectively, before surgery, and 2.79 ± 2.31 (0.00–6.25) and 1.12 ± 1.32 (0.00–3.00) after, reflecting a significant improvement (p = 0.043 and p = 0.028, respectively). NPSI score was 15.29 ± 13.94 before, while it was reduced to 2.29 ± 2.98 afterward (p = 0.028). MPQ's total score was 9.00 ± 3.32 before DBS, achieving 2.71 ± 2.93 after (p = 0.028). Conclusions: DBS improves NMS in generalized inherited/idiopathic dystonia, including chronic pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".