Prevalence of Extracranial venous Narrowing on Magnetic Resonance Venography is Similar in People with Multiple Sclerosis, Their Siblings, and Unrelated Healthy Controls: A Blinded, Case-Control Study
Bibliographic record
Abstract
PURPOSE: The study sought to assess and compare the prevalence of narrowing of the major extracranial veins in subjects with multiple sclerosis and controls, and to assess the sensitivity and specificity of magnetic resonance venography (MRV) for describing extracranial venous narrowing as it applies to the chronic cerebrospinal venous insufficiency theory, using catheter venography (CV) as the gold standard. METHODS: The jugular and azygos veins were assessed with time-of-flight MRV in this assessor-blinded, case-control study of subjects with multiple sclerosis, their unaffected siblings, and unrelated controls. The veins were evaluated by diameter and area, and compared with CV. Collateral vessels were also analyzed for maximal diameter, as a potential indicator of compensatory flow. RESULTS: A high prevalence of extracranial venous narrowing was demonstrated in all study groups, collectively up to 84% by diameter criteria and 90% by area, with no significant difference between the groups when assessed independently (P = .34 and .63, respectively). There was high interobserver variability in the reporting of vessel narrowing (kappa = 0.32), and poor vessel per vessel correlation between narrowing on MRV and CV (kappa = 0.064). Collateral neck veins demonstrated no convincing difference in maximum size or correlation with jugular narrowing. CONCLUSION: There is a high prevalence of narrowing of the major extracranial veins on MRV in all 3 study groups, with no significant difference between them. These findings do not support the chronic cerebrospinal venous insufficiency theory. Although MRV has shown a high sensitivity for identifying venous narrowing, time-of-flight imaging demonstrates poor interobserver agreement and poor specificity when compared with the gold standard CV.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".