Multicentre assessment of motor and sensory evoked potentials in multiple sclerosis: reliability and implications for clinical trials
Bibliographic record
Abstract
BACKGROUND: Motor and sensory evoked potentials (EP) are potential candidate biomarkers for clinical trials in multiple sclerosis. OBJECTIVE: To determine test -retest reliability of motor EP (MEP) and sensory EP (SEP) and associated EP-scores in patients with multiple sclerosis. METHODS: In three centres, 16 relapsing and five progressive multiple sclerosis patients had MEPs and SEPs 1-29 days apart. Five neurophysiologists independently marked latencies by central reading. By variance component analysis, we estimated the critical difference (absolute reliability) for cross-sectional group comparison, comparison of longitudinal group changes, within-subject minimal detectable change and defined within-subject improvement. RESULTS: Cortical SEP responses and cortico-muscular MEP latencies were more reliable than central conduction times. For comparison of 20 subjects per arm, cross-sectional group difference ranged from 0.7 to 3.9 ms and 1.1 to 1.7, group difference in longitudinal changes from 0.4 to 1.8 ms and 0.36 to 0.62, within-subject minimal detectable change from 1.2 to 5.8 ms and 1.2 to 2.0, within-subject improvement from 0.8 to 3.8ms and 0.8 to 1.3, for single EP modalities and EP scores, respectively. CONCLUSIONS: Multicentre EP assessment with central EP reading is feasible and reliable. The critical difference is reasonably low to detect significant group changes and to define responders. The results support the concept of using EP and EP-scores as candidate response biomarkers for quantification of disease progression and for studying remyelination in multiple sclerosis.
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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 | 0.256 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| 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".