Teriflunomide Exhibits Similar Results to Fingolimod in Number Needed to Treat Analysis (P6.171)
Bibliographic record
Abstract
Objective: To analyze the number needed to treat (NNT) to prevent 1 relapse or 1 patient experiencing disability progression in clinical trials of teriflunomide and fingolimod. Background: Teriflunomide, fingolimod, and dimethyl fumarate (DMF), oral disease-modifying therapies (DMTs) for relapsing-remitting MS, have demonstrated efficacy in clinical trials. Clinical trials comparing teriflunomide with oral DMTs have not been performed. Obtaining a measure of comparative efficacy is important to inform clinical decisions. Absolute risk reduction, and its inverse, NNT, can be used to enable cross-trial comparison and is a reliable measure of comparative efficacy. Using post hoc NNT analyses, teriflunomide and DMF demonstrated comparable effects on efficacy, and here, we present similar analyses for teriflunomide compared with fingolimod. Methods: NNT values were derived from studies with teriflunomide 14 mg (TEMSO, NCT00134563; TOWER, NCT00751881) or fingolimod (FREEDOMS, NCT00289978; FREEDOMS II, NCT00355134), based on the inverse of absolute differences between treatment and placebo groups. Results: Across all studies teriflunomide and fingolimod significantly reduced the risk of relapse vs placebo. NNT values to prevent 1 relapse were similar across studies: 5.9 (TEMSO), 5.6 (TOWER), 4.5 (FREEDOMS), 5.3 (FREEDOMS II). Risk of disability progression confirmed for 12 weeks was significantly reduced in TEMSO, TOWER, and FREEDOMS, but not in FREEDOMS II; corresponding NNT values to prevent disability progression were 13.7, 17.1, 15.3, and 23.5, respectively. Conclusions: Using NNT analyses, teriflunomide and fingolimod demonstrated comparable effects on relapse. NNT values to prevent disability progression were similar for both teriflunomide studies and FREEDOMS, but were less favorable in FREEDOMS II. Study supported by: Genzyme, a Sanofi company.
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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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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".