Using measures of corticospinal excitability to map symptom severity in multiple sclerosis
Bibliographic record
Abstract
Background: Transcranial magnetic stimulation (TMS) is a tool used to measure corticospinal excitability. To evaluate the usefulness of TMS as a biomarker in multiple sclerosis (MS), the first step is to examine how well variables derived using TMS align with clinical symptoms of MS. Methods: Participants with MS (n=38) were assigned to motor, cognitive, sensory, or asymptomatic clinical group based on their Expanded Disability Status Scale (EDSS) assessment. Following recording of demographic information, subjective health and scoring of walking and cognition, TMS measures were collected from each brain hemisphere. We first examined whether TMS parameters (resting motor threshold (RMT), active motor threshold (AMT), and cortical silent period (CSP)) would differ among clinical groups. Next, we examined whether TMS parameters predicted severity of symptoms. Results: CSP and AMT in the hemisphere corresponding to the weaker hand predicted measures of symptom severity among people with MS in the motor and cognitive profile groups. Longer CSP was the strongest predictor of slower walking speed (F(1,17)=22.82, p<0.001). Higher AMT was the strongest predictor of cognitive impairment using the Montreal Cognitive Assessment (F(1,17)==25.29, p=0.001) and perceived physical impact of MS using the Multiple Sclerosis Impact Scale-29 (F(1,17)=30.63, p<0.001). Conclusions: CSP and AMT in the hemisphere corresponding to the weaker hand predicted severity of symptoms among people with MS in the motor and cognitive groups. In these cases, TMS variables provided greater predictive value than the traditional EDSS, supporting the use of TMS outcomes as biomarkers in 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".