Association between age and inflammatory disease activity on magnetic resonance imaging in relapse onset multiple sclerosis during long‐term follow‐up
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Inflammatory disease activity in multiple sclerosis (MS) decreases with advancing age. Previous work found a decrease in contrast-enhancing lesions (CELs) with age. Here, we describe the relation of age and magnetic resonance imaging (MRI) measures of inflammatory disease activity during long-term follow-up in a large real-world cohort of people with relapse onset MS. METHODS: We investigated MRI data from the long-term observational Amsterdam MS cohort. We used logistic regression models and negative binomial generalized estimating equations to investigate the associations between age and radiological disease activity after a first clinical event. RESULTS: We included 1063 participants and 10,651 cranial MRIs. Median follow-up time was 6.1 years (interquartile range = 2.4-10.9 years). Older participants had a significantly lower risk of CELs on baseline MRI (40-50 years vs. <40 years: odds ratio [OR] = 0.640, 95% confidence interval [CI] = 0.45-0.90; >50 years vs. <40 years: OR = 0.601, 95% CI = 0.33-1.08) and a lower risk of new T2 lesions or CELs during follow-up (40-50 years vs. <40 years: OR = 0.563, 95% CI = 0.47-0.67; >50 years vs. <40 years: OR = 0.486, 95% CI = 0.35-0.68). CONCLUSIONS: Greater age is associated with a lower risk of inflammatory MRI activity at baseline and during long-term follow-up. In patients aged >50 years, a less aggressive treatment strategy might be appropriate compared to younger patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".