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Record W4415918690 · doi:10.1097/md.0000000000045214

Effectiveness of teriflunomide in patients with relapsing multiple sclerosis who switched from other disease-modifying therapies

2025· article· en· W4415918690 on OpenAlexaboutno aff
Regina Berkovich, Nupur Greene, Sheila R. Reddy, Eunice Chang, Marian H. Tarbox, Sam Fadaee, Cuc Quach, Yelena Pyatkevich

Bibliographic record

VenueMedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTeriflunomideMultiple sclerosisExpanded Disability Status ScaleFingolimodNeurologyMagnetic resonance imagingWilcoxon signed-rank test

Abstract

fetched live from OpenAlex

To examine effectiveness of teriflunomide in patients with multiple sclerosis (MS) who switched to teriflunomide from other disease-modifying therapies (DMTs). Retrospective, observational, pre-post analysis of adults with relapsing MS (RMS; relapsing-remitting MS or active secondary progressive MS [aSPMS]) with prescription for teriflunomide between September 1, 2012, and March 31, 2019, and had been treated with another disease-modifying therapy ("switched"). Data were extracted from medical-chart data from a single US neurology center in California. Index was the date of teriflunomide initiation. Data were extracted at 1-year pre-index, index, and 1- and 2-years post-index. Patients were observed until death, loss to follow-up, or study end. A subgroup of patients with aSPMS were also examined. For inferential comparisons, significance was assessed using paired T-test or Wilcoxon rank sum test, as appropriate. P < .05 was considered significant. Eighty patients with RMS formed the main analysis. At index, mean (±SD) age was 44.0 ± 14.6 years, 71.3% were female, mean duration of MS was 9.3 ± 6.4 years. Mean duration of teriflunomide use was 24.9 ± 14.2 months. Magnetic resonance imaging of lesions were "stable" or "improved" in most patients at baseline (92.5%), at 1-year (95.1%) or 2-years (97.6%). Mean annualized relapse rate decreased by 80.8%, from 0.26 at 1-year pre-teriflunomide initiation to 0.05 at 2-years post-index (P < .001). Mean Expanded Disability Status Scale (EDSS) score slightly increased from 1-year pre-index to 1-year post-index (3.84 vs 3.90, respectively; difference: -0.06 [P = .033]) but was nonsignificant from index to 2-years post-index (3.84 vs 3.94; difference: -0.06 [P = .058]). Montreal Cognitive Assessment and timed 25-foot walk test scores remained stable through follow-up. A decrease in proportion of patients with lymphopenia was recorded from index (30.0%) to 2-years post-index (1.3%). In the subset of patients with aSPMS (n = 32), mean annualized relapse rate reduced from 1-year pre-index to 2-years post-index (0.4 vs 0.03; change: -0.38 [P < .001]). EDSS, Montreal Cognitive Assessment, and timed 25-foot walk test scores remained stable in patients with aSPMS. After switching to teriflunomide, patients with RMS (relapsing-remitting MS or aSPMS) experienced a reduction in relapses and evidence for recovery from lymphopenia. Other markers of disability worsening, including EDSS, remained stable after switching to teriflunomide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.294
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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