Early first‐line treatment response and subsequent disability worsening in relapsing–remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Treatment success in relapsing-remitting multiple sclerosis (RRMS) is generally determined using relapse frequency and magnetic resonance imaging (MRI) activity in the first 6 or 12 months on treatment. The association of these definitions of short-term treatment success with disability worsening and disease activity in the longer term is unclear. In this study, we investigated risk factors associated with early first-line treatment failure in RRMS, and the association of early treatment failure with subsequent disability worsening or "no evidence of disease activity" (NEDA-3) status. METHODS: We used data from CombiRx (clinicaltrials.gov identifier NCT00211887) to investigate risk factors associated with early treatment failure, and the association of early treatment failure at 6 and 12 months with subsequent disability worsening or NEDA-3 at 36 months. RESULTS: CombiRx included 1008 treatment-naïve participants with RRMS, who were randomly assigned to treatment with glatiramer acetate, interferon beta, or the combination of both. Early treatment failure at 6 or 12 months by several definitions was associated with NEDA-3 failure at 36 months, but not with subsequent disability worsening at 36 months. Expanded Disability Status Scale (EDSS) was the only baseline characteristic associated with the risk of disability worsening at 36 months. Approximately 70% of NEDA-3 failures occurred due to MRI activity, and <10% occurred due to EDSS worsening. CONCLUSIONS: Our investigation shows that current definitions of early treatment failure in RRMS are unrelated to patient-relevant disability worsening at 36 months of follow-up. Further research into useful definitions of treatment success and failure in RRMS is needed.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 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".