Use of non-invasive stimulation in movement disorders: a critical review
Bibliographic record
Abstract
BACKGROUND: Noninvasive stimulation has been widely used in the past 30 years to study and treat a large number of neurological diseases, including movement disorders. OBJECTIVE: In this critical review, we illustrate the rationale for use of these techniques in movement disorders and summarize the best medical evidence based on the main clinical trials performed to date. METHODS: A nationally representative group of experts performed a comprehensive review of the literature in order to analyze the key clinical decision-making factors driving transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) in movement disorders. Classes of evidence and recommendations were described for each disease. RESULTS: Despite unavoidable heterogeneities and low effect size, TMS is likely to be effective for treating motor symptoms and depression in Parkinson's disease (PD). The efficacy in other movement disorders is unclear. TMS is possibly effective for focal hand dystonia, essential tremor and cerebellar ataxia. Additionally, it is likely to be ineffective in reducing tics in Tourette syndrome. Lastly, tDCS is likely to be effective in improving gait in PD. CONCLUSIONS: There is encouraging evidence for the use of noninvasive stimulation on a subset of symptoms in selected movement disorders, although the means to optimize protocols for improving positive outcomes in routine clinical practice remain undetermined. Similarly, the best stimulation paradigms and responder profile need to be investigated in large clinical trials with established therapeutic and assessment paradigms that could also allow genuine long-term benefits to be determined.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".