Use of transcranial magnetic stimulation in the treatment of selected movement disorders
Bibliographic record
Abstract
Katlyn E Brown,1 Jason L Neva,2 Noah MH Ledwell,1 Lara A Boyd1,2 1Graduate Program in Rehabilitation Science, 2Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada Abstract: Transcranial magnetic stimulation (TMS) is a valuable technique for assessing the underlying neurophysiology associated with various neuropathologies, and is a unique tool for establishing potential neural mechanisms responsible for disease progression. Recently, repetitive TMS (rTMS) has been advanced as a potential therapeutic technique to treat selected neurologic disorders. In healthy individuals, rTMS can induce changes in cortical excitability. Therefore, targeting specific cortical areas affected by movement disorders theoretically may alter symptomology. This review discusses the evidence for the efficacy of rTMS in Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, and multiple sclerosis. It is hoped that gaining a more thorough understanding of the timing and parameters of rTMS in individuals with neurodegenerative disorders may advance both clinical care and research into the most effective uses of this technology. Keywords: repetitive transcranial magnetic stimulation, selected movement disorders, treatment
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".