An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) is a flexible, non-invasive technique that involves the production of a brief magnetic field to stimulate the conductive structures of the brain. When TMS is specifically employed as a single- or paired-pulse over the motor cortex, the function and integrity of the descending motor pathways can be assessed through the generation of a motor evoked potential (MEP). Important MEP-derived measures include the motor threshold, MEP amplitude and latency, central motor conduction time, silent period, intracortical inhibition, and intracortical facilitation. These functional measures may have use in individuals with multiple sclerosis (MS), a common chronic autoimmune disorder of the central nervous system, due to their useful diagnostic and prognostic implications. As a representation of excitability and conductivity, TMS measures may have the potential to serve as objective markers of corticospinal tract integrity, which is a major aspect of clinical disability in MS. Additionally, TMS may be employed to help monitor and provide insight on the effects of therapies for patients with MS over a longitudinal timeframe. In this review, we focus on the application of TMS in the context of MS, with an emphasis on the relationship between TMS measures and widely used clinical assessment measures used for patients with MS.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".