Transcranial magnetic stimulation enhances the specificity of multiple sclerosis diagnostic criteria: a critical narrative review
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease that involves attacks of inflammatory demyelination and axonal damage, with variable but continuous disability accumulation. Transcranial magnetic stimulation (TMS) is a noninvasive method to characterize conduction loss and axonal damage in the corticospinal tract. TMS as a technique provides indices of corticospinal tract function that may serve as putative MS biomarkers. To date, no reviews have directly addressed the diagnostic performance of TMS in MS. The authors aimed to conduct a critical narrative review on the diagnostic performance of TMS in MS. Methods: The authors searched the Embase, PubMed, Scopus, and Web of Science databases for studies that reported the sensitivity and/or specificity of any reported TMS technique compared to established clinical MS diagnostic criteria. Studies were summarized and critically appraised for their quality and validity. Results: Seventeen of 1,073 records were included for data extraction and critical appraisal. Markers of demyelination and axonal damage-most notably, central motor conduction time (CMCT)-were specific, but not sensitive, for MS. Thirteen (76%), two (12%), and two (12%) studies exhibited high, unclear, and low risk of bias, respectively. No study demonstrated validity for TMS techniques as diagnostic biomarkers in MS. Conclusions: CMCT has the potential to: (1) enhance the specificity of clinical MS diagnostic criteria by "ruling in" true-positives, or (2) revise a diagnosis from relapsing to progressive forms of MS. However, there is presently insufficient high-quality evidence to recommend any TMS technique in the diagnostic algorithm for MS.
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How this classification was reachedexpand
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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".