Subclinical atherosclerosis in multiple sclerosis
Bibliographic record
Abstract
Background People with multiple sclerosis (MS) have an increased risk of ischemic heart disease as compared to people without MS after accounting for traditional vascular risk factors. Objective We assessed whether subclinical atherosclerosis, an inflammatory disease of arteries, occurs in persons with MS who do not have traditional vascular risk factors, and whether the Framingham Score (FRS) predicted carotid intima media thickness (CIMT) similarly in people with and without MS. Methods We recruited participants with and without MS who did not have vascular disease. Participants completed questionnaires, physical assessments, underwent an ultrasound (CIMT), and provided samples for HbA1c and lipid measurements. We defined subclinical atherosclerosis as an average CIMT ≥75th percentile, and tested the association between MS/not-MS, FRS, and atherosclerosis using logistic regression. Results We recruited 106 participants with MS 101 without MS. The average (SD) CIMT did not differ between the MS (0.60 [0.11]) and non-MS (0.61 [0.12]) cohorts (p = 0.69), nor did the proportion with atherosclerosis (MS: 11.3% vs. non-MS 13.4%, p = 0.58). On regression analysis a 1-point increase in the FRS was associated with 11% increased odds of having atherosclerosis (95%CI: 1.04, 1.19) but MS was not. Conclusion MS was not associated with subclinical atherosclerosis.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".