Aging is associated with reduced inflammatory disease activity independent of disease duration in relapsing multiple sclerosis trial populations
Bibliographic record
Abstract
BACKGROUND: Higher age is associated with less inflammatory disease activity in relapsing-remitting multiple sclerosis (RRMS). It is unknown whether age itself or disease duration underlies this association. OBJECTIVES: This study investigated the effects of age, disease duration, and inflammatory disease activity in people with RRMS. METHODS: Individual patient-level data from five large phase III randomized controlled trials (RCTs) was utilized to investigate the association of both age and disease duration with annualized relapse rate (ARR), contrast-enhancing lesions (CELs), and new T2 lesions on magnetic resonance imaging (MRI) at baseline and follow-up. RESULTS: The data set included 5626 participants. Higher age was associated with lower ARRs, lower CEL number on MRI at baseline and follow-up, and lower new T2 lesion numbers at follow-up. This effect was present in all disease duration groups. For example, we found a lower number of new T2 lesions on MRI during follow-up in higher age groups compared to lower age groups, independent of disease duration. CONCLUSION: Aging in RRMS is associated with a lower risk of inflammatory disease activity, across different disease durations. Age should be taken into account when designing clinical trials and future research should investigate how age should be integrated into personalized predictions of treatment response and risk profiling.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".