Moderate–high efficacy disease-modifying therapies reduce relapse risk in late-onset multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: Late-onset multiple sclerosis (LOMS) now comprises over 10% of MS diagnoses in contemporary cohorts. The effectiveness of disease-modifying therapies (DMTs) in LOMS is unclear. We aimed to establish the comparative effectiveness of moderate-high-efficacy versus low-efficacy DMTs in LOMS. METHODS: Using data from the MSBase registry, this multicentre cohort study included people with MS with symptom onset after age 50. Covariates were balanced using inverse-probability-treatment-weighting (IPTW). Primary outcomes were time to first relapse and annualised relapse rate (ARR). Secondary outcomes were 6-month confirmed disability progression (CDP), confirmed disability improvement (CDI), relapse-associated worsening (RAW) and progression independent of relapse activity (PIRA). RESULTS: Of 1032 participants, 472 received moderate-high-efficacy DMTs and 560 received low-efficacy DMTs. IPTW-weighted ARR was 0.06 for moderate-high-efficacy and 0.09 for low-efficacy DMTs, corresponding to an ARR ratio of 0.68 (95% CI 0.50 to 0.93, p=0.01). HR for time to first relapse was 0.66 (95% CI 0.47 to 0.91, p=0.01) in favour of moderate-high-efficacy DMTs.Among 856 participants with adequate follow-up, 37% experienced CDP over a median of 4.43 years, with most events (83.6%) attributable to PIRA. The HR for time to CDP was 0.78 (p=0.08) and RAW was 0.69 (p=0.31) in favour of moderate-high-efficacy DMTs, though neither reached statistical significance. There was no difference in CDI or PIRA. CONCLUSION: Moderate-high-efficacy DMTs reduced relapse risk in LOMS. Relapse activity was low. CDP was common and driven by PIRA. Although the CDP and RAW results did not reach statistical significance, the overall findings support the initial use of moderate-high-efficacy DMTs in LOMS.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".