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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".