Typical and Emerging Diagnostic MRI Features in Multiple Sclerosis
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) stands as the most sensitive paraclinical technique for detecting the demyelinating lesions characteristic of multiple sclerosis (MS). Consequently, MRI plays a pivotal role in establishing an accurate and timely diagnosis of the disease, ultimately based on the application of the McDonald criteria. Early diagnosis is particularly important as it facilitates the prompt initiation of disease-modifying treatments, deemed most effective during the initial phases of MS. This review article examines the recommended standardized MRI protocol, as well as the classic imaging features of MS in the brain, optic nerve, and spinal cord, capable of discriminating, in most cases, MS from other disorders that can mimic this disease. Additionally, novel MR imaging findings, such as the central vein sign and paramagnetic rim lesion, which have been proposed as new imaging biomarkers to enhance diagnostic specificity for MS, are also discussed. These emerging features are likely to be incorporated in the future iterations of the McDonald criteria, and therefore, radiologists should be familiar with their appearance and with the optimal MRI protocols required for their detection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".