Chronic cerebrospinal venous insufficiency in multiple sclerosis: clinical correlates from a multicentre study
Bibliographic record
Abstract
BACKGROUND: Chronic cerebrospinal venous insufficiency (CCSVI) has recently been reported to be associated with multiple sclerosis (MS). However, its actual prevalence, possible association with specific MS phenotypes, and potential pathophysiological role are debated. METHOD: We analysed the clinical data of 710 MS patients attending six centres (five Italian and one Canadian). All were submitted to venous Doppler sonography and diagnosed as having or not having CCSVI according to the criteria of Zamboni et al. RESULTS: Overall, CCSVI was diagnosed in 86% of the patients, but the frequency varied greatly between the centres. Even greater differences were found when considering singly the five diagnostic criteria proposed by Zamboni et al. Despite these differences, significant associations with clinical data were found, the most striking being age at disease onset (about five years greater in CCSVI-positive patients) and clinical severity (mean EDSS score about one point higher in CCSVI-positive patients). Patients with progressive MS were more likely to have CCSVI than those with relapsing-remitting MS. CONCLUSION: The methods for diagnosing CCSVI need to be refined, as the between-centre differences, particularly in single criteria, were excessively high. Despite these discrepancies, the strong associations between CCSVI and MS phenotype suggest that the presence of CCSVI may favour a later development of MS in patients with a lower susceptibility to autoimmune diseases and may increase its severity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".