Brain and cervical spinal cord MRI correlates of sensorimotor impairment in patients with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Cervical spinal cord (cSC) lesions and atrophy contribute to disability in multiple sclerosis (MS), but associations with specific sensorimotor dysfunction require further exploration. OBJECTIVE: To investigate the associations of brain and cSC magnetic resonance imaging (MRI) measures with sensorimotor impairment in MS. METHODS: One hundred fifty-one MS patients and 69 healthy controls underwent 3T MRI and clinical assessments including Expanded Disability Status Scale (EDSS), 9-hole peg test (9-HPT), finger tapping test (FTT), timed 25-foot walk test (T25FWT), and vibration detection threshold (VDT). Random forest ranked brain (T2-hyperintense lesion volume (T2-LV) and normalized deep gray matter (GM), cortical and white matter (WM) volumes) and cSC (T2-LV and total, GM, and WM cross-sectional areas (CSAs) at C2/C3 level) MRI measures relevance in explaining EDSS milestones (EDSS ⩾3.0, ⩾4.0, and ⩾6.0), VDT, pyramidal and sensory functional systems (P-FS and S-FS ⩾2), and motor tests impairment. RESULTS: = 0.194), and impaired P-FS (AUC = 0.820), S-FS (AUC = 0.795), 9-HPT (AUC = 0.793), FTT (AUC = 0.740), and T25FWT (AUC = 0.825). cSC GM CSA was the most informative feature for all outcomes, except 9-HPT. CONCLUSION: cSC MRI measures, especially GM CSA, explain EDSS and sensorimotor dysfunction better than brain measures in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".