Venocentric Lesions: An MRI Marker of MS?
Bibliographic record
Abstract
From the earliest descriptions of multiple sclerosis (MS), the venocentric characteristic of plaques was noted. Recently, numerous magnetic resonance imaging (MRI) studies have proposed this finding as a prospective biomarker for MS, which might aid in differentiating MS from other diseases with similar MRI findings. High-field MRI studies have shown that penetrating veins can be detected in most MS lesions using T2(∗) weighted or susceptibility-weighted imaging. Future studies must address the feasibility of imaging such veins in a clinically practical context. The specificity of this biomarker has been studied only in a limited capacity. Results in microangiopathic lesions are conflicting, whereas asymptomatic white matter hyperintensities as well as lesions of neuromyelitis optica are less frequently venocentric compared to MS plaques. Prospective studies have shown that the presence of venocentric lesions at an early clinical presentation is highly predictive of future MS diagnosis. This is very promising, but work remains to be done to confirm or exclude lesions of common MS mimics, such as acute disseminate encephalomyelitis, as venocentric. A number of technical challenges must be addressed before the introduction of this technique as a complementary tool in current diagnostic procedures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".