The Central Vein Sign in Radiologically Isolated Syndrome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Radiologically isolated syndrome describes asymptomatic individuals with incidental radiologic abnormalities suggestive of multiple sclerosis. Recent studies have demonstrated that >40% of white matter lesions in MS (and often substantially more) have visible central veins on MR imaging. This “central vein sign” reflects perivenous inflammatory demyelination and can assist in differentiating MS from other white matter disorders. We therefore hypothesized that >40% of white matter lesions in cases of radiologically isolated syndrome would show the central vein sign. MATERIALS AND METHODS: We recruited 20 participants diagnosed with radiologically isolated syndrome after evaluation by a neurologist. We performed 3T MR imaging of the brain and cervical spinal cord. White matter lesions were analyzed for the central vein sign. RESULTS: Of 391 total white matter lesions, 292 (75%) demonstrated the central vein sign (central vein sign+). The median proportion of central vein sign+ lesions per case was 87% (range, 29%–100%). When the “40% rule” that has been proposed to distinguish MS from other disorders was applied, of 20 participants, 18 cases of radiologically isolated syndrome (90%) had ≥40% central vein sign+ lesions (range, 55%–100%). Two participants (10%) had <40% central vein sign+ lesions (29% and 31%). When the simpler “rule of 6” was applied, 19 participants (95%) met these criteria. In multivariable models, the number of spinal cord and infratentorial lesions was associated with a higher proportion of central vein sign+ lesions (P = .002; P = .06, respectively). CONCLUSIONS: Most cases of radiologically isolated syndrome had a high proportion of central vein sign+ lesions, suggesting that lesions in these individuals reflect perivenous inflammatory demyelination. Moreover, we found correlations between the proportion of central vein sign+ lesions and spinal cord lesions, a known risk factor for radiologically isolated syndrome progressing to MS. These findings raise the possibility, testable prospectively, that the central vein sign may have prognostic value in distinguishing patients with radiologically isolated syndrome at risk of developing clinical MS from those with white matter lesions of other etiologies.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".