Transcranial Magnetic Stimulation as a Potential Biomarker in Multiple Sclerosis: A Systematic Review with Recommendations for Future Research
Bibliographic record
Abstract
Multiple sclerosis (MS) is a demyelinating disorder of the central nervous system. Disease progression is variable and unpredictable, warranting the development of biomarkers of disease status. Transcranial magnetic stimulation (TMS) is a noninvasive method used to study the human motor system, which has shown potential in MS research. However, few reviews have summarized the use of TMS combined with clinical measures of MS and no work has comprehensively assessed study quality. This review explored the viability of TMS as a biomarker in studies of MS examining disease severity, cognitive impairment, motor impairment, or fatigue. Methodological quality and risk of bias were evaluated in studies meeting selection criteria. After screening 1603 records, 30 were included for review. All studies showed high risk of bias, attributed largely to issues surrounding sample size justification, experimenter blinding, and failure to account for key potential confounding variables. Central motor conduction time and motor-evoked potentials were the most commonly used TMS techniques and showed relationships with disease severity, motor impairment, and fatigue. Short-latency afferent inhibition was the only outcome related to cognitive impairment. Although there is insufficient evidence for TMS in clinical assessments of MS, this review serves as a template to inform future research.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".