Association of age and inflammatory disease activity in the pivotal natalizumab clinical trials in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Focal inflammatory disease activity in relapsing-remitting multiple sclerosis (RRMS) diminishes with increasing age. Here we use patient-level data from randomised controlled trials (RCTs) of natalizumab treatment in RRMS to investigate the association of age and inflammatory disease activity. METHODS: We used patient-level data from the AFFIRM (natalizumab vs placebo in relapsing-remitting MS, NCT00027300) and SENTINEL (natalizumab plus interferon beta vs interferon beta in relapsing remitting MS, NCT00030966) RCTs. We determined the proportion of participants developing new T2 lesions, contrast-enhancing lesions (CELs) and relapses over 2 years of follow-up as a function of age, and investigated the association of age with time to first relapse using time-to-event analyses. RESULTS: At baseline, there were no differences between age groups in T2 lesion volume and number of relapses in the year before inclusion. In SENTINEL, older participants had a significantly lower number of CELs. During both trials, the number of new CELs and the proportion of participants developing new CELs were significantly lower in older age groups. The number of new T2 lesions and the proportion of participants with any radiological disease activity during follow-up were also lower in older age groups, especially in the control arms. CONCLUSIONS: Older age is associated with a lower prevalence and degree of focal inflammatory disease activity in treated and untreated RRMS. Our findings inform the design of RCTs, and suggest that patient age should be taken into consideration when deciding on immunomodulatory treatment in RRMS.
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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 | 0.060 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".