Characterizing Long-term Disability Progression and Employment in NARCOMS Registry Participants with Multiple Sclerosis Taking Dimethyl Fumarate
Bibliographic record
Abstract
BACKGROUND: Delayed-release dimethyl fumarate (DMF) is effective in relapsing-remitting multiple sclerosis (RRMS), but long-term effects of DMF on disability and disease progression in clinical settings are unknown. We evaluated disability and employment outcomes in persons with RRMS treated with DMF for up to 5 years. METHODS: This longitudinal study included US North American Research Committee on Multiple Sclerosis (NARCOMS) Registry participants with RRMS reporting DMF initiation in fall 2013 through spring 2018 with 1 year or more of follow-up. Time to 6-month confirmed disability progression (≥1-point increase in Patient-Determined Disease Steps [PDDS] score) and change in employment status were evaluated using Kaplan-Meier analysis. Participants were censored at last follow-up or at DMF discontinuation, whichever came first. RESULTS: During the study, 725 US participants with RRMS had at least 1 year of DMF follow-up data, of whom most were female and White. At year 5, 69.9% (95% CI, 65.4%-73.9%) of these participants were free from 6-month confirmed disability progression, and 84.7% (95% CI, 78.6%-89.2%) were free from conversion to secondary progressive MS. Of 116 participants with data at baseline and year 5, most had stable or improved PDDS and Performance Scales scores over 5 years. Of 322 participants 62 years and younger and employed at the index survey, 66.0% (95% CI, 57.6%-73.1%) were free from a negative change in employment type over 5 years. CONCLUSIONS: Most US NARCOMS Registry participants treated up to 5 years with DMF remained free from 6-month confirmed disability progression and conversion to secondary progressive MS and had stable disability and employment status. These results support the long-term stability of disability and work-related outcomes with disease-modifying therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".