Magnetic resonance imaging signatures of vascular pathology in multiple sclerosis
Bibliographic record
Abstract
Venous vascular contributing factors to multiple sclerosis (MS) have been known for some time. Only recently has the scope of their potential role become more apparent with the theory of chronic cerebrospinal venous insufficiency (CCSVI). As research expands to further explore the role of vascular pathology in the MS population, it is expedient to review the evidence from an imaging perspective. In this paper, we review the current state-of-the-art methods using magnetic resonance imaging (MRI) as applied to imaging MS patients and CCSVI. This includes evaluating imaging signatures of vascular structure and flow as well as brain iron content. Upon review of the literature, we find that extracranial venous anomalies including stenosis, venous malformations, and collateralization of flow in the major veins of the neck have been observed to be prevalent in the MS population. Abnormal flow has been reported in MS patients both in major vessels using phase-contrast flow quantification and in the brain using perfusion-weighted imaging. We discuss the role of quantitative flow imaging and its potential in assessing possible biomarkers for abnormal flow. Finally, it has been suggested that the presence of high iron content may indirectly indicate progression of existing vascular pathology. To that end, we review the use of susceptibility-weighted imaging in monitoring iron in the thalamus, basal ganglia, and MS lesions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".