Relapses in multiple sclerosis are age- and time-dependent
Bibliographic record
Abstract
OBJECTIVES: To examine the relative relapse-rate patterns over time in a relapsing multiple sclerosis (MS) cohort and to investigate potential predictors of relapse rates and periods of low-relapse activity. METHODS: This retrospective cohort study followed 2477 relapsing-remitting (RR) MS patients from onset to 1 July 2003. Annualised relapse rates were examined according to sex, age at onset, the patient's current age and disease duration. The relationship between relapse rates and baseline characteristics (sex, onset age and onset symptoms) were examined using Poisson regression. Time to the first 5 years relapse-free was examined using Kaplan-Meier survival analysis. RESULTS: The mean follow-up time (from onset of MS symptoms) was 20.6 years, during which time 11,722 post-onset relapses were recorded. The relapse rate decreased by 17% every 5 years (between years 5 to 30 post-onset), but this decline increased in magnitude with increasing onset age. Women and those with onset sensory symptoms exhibited a higher relapse rate (p< or =0.001). More than three-quarters of patients (1692/2189) experienced a 5-year relapse-free period during the RR phase. CONCLUSION: Relapse rates were age- and time-dependent. Our observations have clinical implications: 1) any drug able to modify relapse rates has the greatest potential for a population impact in patients <40 years old and within the first few demi-decades of disease; 2) continuation of drug beyond these times may be of limited value; 3) long-term follow-up studies must consider that relapse rates probably decline at different rates over time according to the patient's onset age; 4) a relapse-quiescent period in MS is not uncommon.
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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 | 0.001 | 0.007 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".