Comparison of first-line fingolimod efficacy compared with first-line interferon-beta or glatiramer therapy in MS patients with active disease using propensity-matched registry data (P3.247)
Bibliographic record
Abstract
OBJECTIVE: Investigate time to relapse and discontinuation in a propensity-matched sample of MS patients on first-line fingolimod compared with first line interferon-beta (IFNβ) or glatiramer acetate (GA) following relapse. BACKGROUND: Outcomes in patients initiating first-line fingolimod relative to IFNβ or GA following relapse are not known. Comparisons of treatment effectiveness from observational registry data can be confounded as treatment assignments are non-random. Propensity score matching is a statistical technique to adjust for covariate imbalances across cohorts. DESIGN/METHODS: The MSBase study is a global, longitudinal registry for Multiple Sclerosis. At time of data extraction, the registry contained 31,429 patients from 104 centres across 30 countries. All patients included in the analysis had at least one relapse in the 12 months prior to baseline. First line fingolimod initiations were 1:1 propensity matched to first-line IFNβ/GA commencements using sex, age, country, disease duration, EDSS and pre-treatment relapse activity as baseline matching characteristics. Predictors of time to first relapse and time to treatment discontinuation were investigated using a clustered marginal Cox model. RESULTS: A total of 180 first-line fingolimod patients were successfully matched to 180 IFNβ/GA commencements. Relapse rate in first line fingolimod was 18.4 relapse per 100 person-years (95[percnt] CI 13.8-24.7) compared with 25.1 relapse per 100 person-years with first-line IFNβ/GA (95[percnt] CI 20.8, 30.2). First line fingolimod was associated with a 46[percnt] reduction in the rate of on-treatment relapse compared with first-line IFNβ/GA (HR 0.54, 95[percnt] CI 0.35-0.83). Similarly first-line fingolimod was associated with a 42[percnt] reduction in treatment discontinuation compared with IFNβ/GA (HR 0.58, 95[percnt] CI 0.34, 0.96). The restricted sample size did not permit meaningful comparisons of confirmed disability progression. CONCLUSIONS: The efficacy of fingolimod initiation, as assessed by time to first relapse and treatment discontinuation, was superior to that of IFNβ/GA in a first-line setting in propensity-matched MS patients.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".