Baseline characteristics associated with NEDA-3 status in fingolimod-treated patients with relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
Fingolimod is an efficacious treatment for relapsing-remitting multiple sclerosis (RRMS) and there is class I evidence that it is superior to standard care in reducing relapse rate. However, real-world data investigating its effectiveness and potential predictors of response are still scarce. To estimate (i) the proportion of fingolimod-treated patients who achieved the no evidence of disease activity (NEDA-3) status; and (ii) to determine which baseline (i.e. at treatment start) clinical and magnetic resonance imaging (MRI) variables were associated with better outcomes. We collected clinical and MRI data of RRMS patients treated with fingolimod and followed-up for 24 months. The proportion of patients who had NEDA-3 - i.e. absence of relapses, sustained Expanded Disability Status Scale (EDSS) worsening and radiological activity on MRI - was estimated. A Cox proportional hazard model was carried out to investigate which baseline characteristics were associated with the NEDA status at follow-up. We collected data of 201 patients who started fingolimod. Of them, 24 (12%) were treatment-naïve, 115 (58%) were switched after failing a self-injectable drug, and 60 (30%) switching from natalizumab. Five patients who discontinued fingolimod early (within 3 months) (bradycardia, n = 2; leukopaenia, n = 2; macular oedema, n = 1) were removed from the analysis. At follow-up, 118 (60%) patients achieved the NEDA-3 status, while 78 experienced clinical and/or MRI activity. The risk of not achieving the NEDA-3 status was associated with higher baseline EDSS score (hazard ratio [HR] = 1.18, p = 0.024) and more relapses in the 12 months prior to fingolimod start (HR = 1.61, p = 0.014). Our findings suggest that fingolimod may lead to a better control of the disease if started in patients with a less aggressive disease (i.e. fewer pre-treatment relapses and milder disability level), thus supporting its possible role as an early treatment for MS.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".