Transcranial Magnetic Stimulation as a Potential Biomarker in Multiple Sclerosis: A Systematic Review with Recommendations for Future Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it