Teriflunomide reduces relapse-related neurological sequelae, hospitalizations and steroid use
Bibliographic record
Abstract
Multiple sclerosis (MS) relapses impose a substantial clinical and economic burden. Teriflunomide is a new oral disease-modifying therapy approved for the treatment of relapsing MS. We evaluated the effects of teriflunomide treatment on relapse-related neurological sequelae and healthcare resource use in a post hoc analysis of the Phase III TEMSO study. Confirmed relapses associated with neurological sequelae [defined by an increase in Expanded Disability Status Scale/Functional System (sequelae-EDSS/FS) ≥ 30 days post relapse or by the investigator (sequelae-investigator)] were analyzed in the modified intention-to-treat population (n = 1086). Relapses requiring hospitalization or intravenous (IV) corticosteroids, all hospitalizations, emergency medical facility visits (EMFV), and hospitalized nights for relapse were also assessed. Annualized rates were derived using a Poisson model with treatment, baseline EDSS strata, and region as covariates. Risks of sequelae and hospitalization per relapse were calculated as percentages and groups were compared with a χ(2) test. Compared with placebo, teriflunomide reduced annualized rates of relapses with sequelae-EDSS/FS [7 mg by 32 % (p = 0.0019); 14 mg by 36 % (p = 0.0011)] and sequelae-investigator [25 % (p = 0.071); 53 % (p < 0.0001)], relapses leading to hospitalization [36 % (p = 0.015); 59 % (p < 0.0001)], and relapses requiring IV corticosteroids [29 % (p = 0.001); 34 % (p = 0.0003)]. Teriflunomide-treated patients spent fewer nights in hospital for relapse (p < 0.01). Teriflunomide 14 mg also decreased annualized rates of all hospitalizations (p = 0.01) and EMFV (p = 0.004). The impact of teriflunomide on relapse-related neurological sequelae and relapses requiring healthcare resources may translate into reduced healthcare costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".