Quantitative Flow Measurements in the Internal Jugular Veins of Multiple Sclerosis Patients Using Magnetic Resonance Imaging
Bibliographic record
Abstract
PURPOSE: To study the blood flow through the internal jugular veins (IJVs) of the MS population. MATERIALS AND METHODS: Two hundred MS patients and 14 normal volunteers were evaluated with magnetic resonance imaging (MRI) at 3T. Contrast-enhanced time-resolved 3D MR angiography and 2D time-of-flight imaging were performed to assess abnormalities in the extracranial vascular anatomy. Based on this assessment, the MS population was divided into subgroups of non-stenotic (NST), cervical 1 stenotic only (C1ST) and cervical 6 stenotic (C6ST) subjects. In this study, 2D phase contrast MR imaging was used to quantify blood flow through major veins and arteries in the neck and flow differences among the groups were analyzed. RESULTS: Of the 200 MS patients, 87 (43.5%) belonged to the NST group, 50 (25%) belonged to the C1ST group and 63 (31.5%) belonged to the C6ST group. The total IJV flow normalized to the total arterial flow of the NST group was 75.12 ± 12.22 %. This was significantly higher than that of the C1ST group, 63.93 ± 16.08 % (p < 0.0001), which in turn was significantly higher than that of the C6ST group, 52.13 ± 20.71 % (p = 0.001). Seventy-nine percent of the stenotic groups had a normalized subdominant IJV flow of less than 20%, a combined IJV flow of less than 5o% and/or a sub-dominant IJV flow vs. dominant IJV flow ratio of less than 1/3. Only 2% of the NST group had a combined IJV flow of less than 50%, compared to 35% of the stenotic groups. CONCLUSION: Blood flow through the IJVs was reduced in the MS population with stenoses compared to those without.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".