The ACROSS study: Long-term efficacy of fingolimod in patients with relapsing–remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: In chronic diseases such as multiple sclerosis requiring lifelong treatment, studies on long-term outcomes are important. OBJECTIVE: To assess disability and magnetic resonance imaging-related outcomes in relapsing multiple sclerosis patients from a Phase 2 study of fingolimod 10 or more years after randomization and to compare outcomes in patients who had a higher fingolimod exposure versus those with a lower fingolimod exposure. METHODS: ACROSS was a cross-sectional follow-up study of patients originally enrolled in a Phase 2 fingolimod proof-of-concept study (NCT00333138). Disability and magnetic resonance imaging-related outcomes were assessed in patients grouped according to fingolimod treatment duration, based on an arbitrary cut-off: ≥8 years (high exposure) and <8 years (low exposure). RESULTS: Overall, 175/281 (62%) patients participated in ACROSS; 104 (59%) of these were classified "high exposure." At 10 years, patients in the high-exposure group had smaller increases in Expanded Disability Status Scale (+0.55 vs. +1.21), and lower frequencies of disability progression (34.7% vs. 56.1%), wheelchair use (4.8% vs. 16.9%), or transition to secondary progressive multiple sclerosis (9.6% vs. 22.5%) than those in the low-exposure group. The high-exposure patients also had less progression in most magnetic resonance imaging-related outcomes. CONCLUSION: After 10 years of fingolimod treatment, disability progression was lower in the high-exposure group than in the low-exposure group.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".