Lower corticospinal excitability and greater fatigue among people with multiple sclerosis experiencing pain
Bibliographic record
Abstract
Introduction: Persons with multiple sclerosis (MS) frequently report pain that negatively affects their quality of life. Evidence linking pain and corticospinal excitability in MS is sparse. We aimed to (1) examine differences in corticospinal excitability in MS participants with and without pain and (2) explore predictors of pain. Methods: Sixty-four participants rated their pain severity on a visual analog scale (VAS). Transcranial magnetic stimulation (TMS) and validated clinical instruments characterized corticospinal excitability and subjective disease features like mood and fatigue. We retrieved information on participants' prescriptions and disability status from their clinical records. Results: Fifty-five percent of participants reported pain that affected their daily functioning. Persons with pain had significantly greater fatigue and lower area under the excitatory motor evoked potential (MEP) recruitment curve (eREC AUC), a measure of total corticospinal excitability. After controlling for age, disability status, and pain medications, increased fatigue and decreased eREC AUC together explained 40% of the variance in pain. Discussion: Pain in MS is multifactorial and relates to both greater fatigue and lesser corticospinal excitability. Future work should better characterize relationships between these outcomes to develop targeted pain interventions such as neuromodulation. Summary: We examined pain in MS. Individuals with pain had higher fatigue and lower corticospinal excitability than those without pain. These outcomes significantly predicted self-reported pain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".