Impact of multiple sclerosis relapses on progression diminishes with time
Bibliographic record
Abstract
OBJECTIVE: The relationship between relapses and long-term disability in multiple sclerosis (MS) remains to be fully elucidated. Current literature is conflicting and focused on early relapses. We investigated the effects of relapses at different stages on disability progression. METHODS: We conducted a retrospective review of 2,477 patients with definite relapsing-onset MS followed until July 2003 in British Columbia, Canada. Time-dependent Cox proportional hazards models examined the effect of relapses at different time periods (0-5; >5-10; >10 years postonset) on time to cane (Expanded Disability Status Scale [EDSS]) and secondary progressive MS (SPMS). Findings were derived from hazard ratios with 95% confidence intervals (CIs), adjusted for sex, onset age, and symptoms. RESULTS: Mean follow-up was 20.6 years; 11,722 postonset relapses were recorded. An early relapse (within 5 years postonset) was associated with an increased hazard in disease progression over the short term, by 48%; 95% CI 37%-60% for EDSS 6 and 29%; 95% CI 20%-38% for SPMS. However, this substantially lessened to 10%; 95% CI 4%-16% (EDSS 6) and 2%; 95% CI -2%-7% (SPMS) after 10 years postonset. The impact of later relapses (>5-10 years postonset) also lessened over time. Effects were modulated by age, impact being greatest in younger (<25 years at onset) and least in older (>or=35 years) patients where relapses beyond 5-years postonset typically failed to reach significance. Relapses during SPMS had no measurable impact on time to EDSS 6 from SPMS. CONCLUSION: Relapses within the first 5 years of disease impacted on disease progression over the short term. However, the long-term impact was minimal, either for early or later relapses. Long-term disease progression was least affected by relapses in patients with an extended disease duration (>10 years) or already in the secondary progressive phase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".