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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".