Cervical Spinal Cord Magnetization Transfer Ratio and Its Relationship With Clinical Outcomes in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: The cervical spinal cord (cSC) is highly relevant to clinical dysfunction in multiple sclerosis (MS) but remains understudied using quantitative magnetic resonance imaging (MRI). We assessed magnetization transfer ratio (MTR), a semi-quantitative MRI measure sensitive to MS-related tissue microstructural changes, in the cSC and its relationship with clinical outcomes in radiologically isolated syndrome (RIS) and MS. METHODS: MTR data were acquired from 52 RIS, 201 relapsing-remitting MS (RRMS), 47 primary progressive MS (PPMS), and 43 control (CON) participants across four sites in the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo) using 3.0 T MRI systems. Mean MTR was compared between groups in whole cSC and sub-regions between C2-C4. Multiple linear regression was used to evaluate relationships between MTR and clinical outcomes, including the expanded disability status scale (EDSS), walking speed test (WST), and manual dexterity test (MDT). RESULTS: There were consistent group differences in MTR, which were most pronounced between PPMS and CON (-5.8% to -3.7%, p ≤ 0.01). In PPMS, lower MTR was associated with greater disability as measured by EDSS (β = -0.3 to -0.1, p ≤ 0.03), WST (β = -0.9 to -0.5, p ≤ 0.04), and MDT (β = -0.6 and - 0.5, p = 0.04). In RRMS, MTR was associated with only EDSS (β = -0.1, p ≤ 0.03). INTERPRETATION: In this large sample of RIS and MS, cSC MTR was lowest in PPMS, with associations between MTR and clinical outcomes in MS but not RIS. These findings suggest that MTR provides important information about the underlying tissue microstructural integrity of the cSC relevant to clinical disability in established 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".