Motor Evoked Potential—A Pilot Study Looking at Reliability and Clinical Correlations in Multiple Sclerosis
Bibliographic record
Abstract
PURPOSE: Multiple sclerosis (MS) is a clinically heterogeneous disease. Biomarkers that can assess pathological processes that are unseen with conventional imaging remain an unmet need in MS disease management. Motor evoked potentials (MEPs) could be such a biomarker. To determine and follow longitudinal MEP reliability and correlations with clinical measures in MS patients. METHODS: This is a single-center study in alemtuzumab-treated MS patients to evaluate temporal reliability of MEPs, identify MEP minimum detectible differences, and explore correlations with existing clinical scales. Ten MS patients recently treated with alemtuzumab were evaluated every 6 months over 3 years. Clinical evaluations consisted of expanded disability status scale, timed 25-foot walk, 6-minute walk, and nine-hole peg test. MEPs were measured twice, 2 weeks apart, every 6 months. RESULTS: Eight patients completed all 3 years of study. The intraclass correlation coefficient for MEP parameters ranged from 0.76 to 0.98. TA latency and amplitude with facilitation significantly and strongly correlated with all clinical measures, whereas the MEP duration modestly correlated. Biceps latency with facilitation significantly and moderately correlated with 9-hole peg test. Longitudinal correlations demonstrated good predictive values for either clinical deterioration or improvement. CONCLUSIONS: MEPs have excellent intrapatient and intrarater reliability, and TA MEPs significantly and strongly correlated with expanded disability status scale, 6-minute walk, and timed 25-foot walk, whereas biceps MEPs significantly and moderately correlated with nine-hole peg test. Further studies using larger cohorts of MS patients are indicated. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, Identifier: NCT02623946.
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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 | 0.002 | 0.006 |
| 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) | 0.001 | 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".