Spinal cord quantitative MRI discriminates between disability levels in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: The clinicoradiologic paradox, or disconnect between clinical and radiologic findings, is frequently encountered in multiple sclerosis (MS), particularly in the spinal cord (SC), where lesions are expected to cause clinical impairment. We aimed to assess whether quantitative diffusion tensor and magnetization transfer imaging measures in the SC can distinguish MS cases of comparable lesion burdens with high and low disability. METHODS: One hundred twenty-four patients with MS underwent 3-T cervical SC MRI and were categorized into 4 subgroups according to SC lesion count and disability level. Regions of interest circumscribed the SC cross-section axially between C3 and C4. Cross-sectional area, fractional anisotropy (FA), mean diffusivity (MD), perpendicular diffusivity (λ(⊥)), parallel diffusivity (λ(‖)), and magnetization transfer ratio (MTR) were calculated. Differences between patient subgroups were assessed using t tests and linear regression. RESULTS: FA, MD, λ(⊥), λ(‖), MTR, and SC cross-sectional area were more abnormal in the high- vs low-disability subgroup of patients with low lesion counts (p < 0.05). MRI measures (except λ(‖) and MTR) were more abnormal in the high- vs low-disability subgroup of patients with high lesion counts (p < 0.05). In age- and sex-adjusted comparisons of high- vs low-disability subgroups, all MRI measures retained differences in the low-lesion subgroup, except λ(‖), whereas only FA, MD, and λ(⊥) retained differences in the high-lesion subgroup. CONCLUSIONS: In this cross-sectional study of patients with MS, quantitative MRI reflects clinically relevant differences beyond what can be detected by conventional MRI. Our findings support the utility of quantitative MRI in clinical settings, where accurate measurement of disease burden is becoming increasingly critical for assessing treatment efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".