Surgical treatment of dystonia
Bibliographic record
Abstract
INTRODUCTION: Treatment of dystonia should be individualized and tailored to the specific needs of patients. Surgical treatment is an important option in medically refractory cases. Several issues regarding type of the surgical intervention, targets, and predict factors of benefit are still under debate. Areas covered: To date, several clinical trials have proven the benefit and safety of deep brain stimulation (DBS) for inherited and idiopathic isolated dystonia, whereas there is still insufficient evidence in combined and acquired dystonia. The globus pallidus internus (GPi) is the target with the best evidence, but data on the subthalamic nucleus seems also to be promising. Evidence suggests that younger patients with shorter disease duration experience greater benefit following DBS. Pallidotomy and thalamotomy are currently used in subset of carefully selected patients. The development of MRI-guided focused ultrasound might bring new options to ablation approach in dystonia. Expert commentary: GPi-DBS is effective and safe in isolated dystonia and should not be delayed when symptoms compromise quality of life and functionality. Identifying the best candidates to surgery on acquired and combined dystonias is still necessary. New insights about pathophysiology of dystonia and new technological advances will undoubtedly help to tailor surgery and optimize clinical effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".