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Record W2596347108 · doi:10.1177/1352458517695468

The efficacy of teriflunomide in patients who received prior disease-modifying treatments: Subgroup analyses of the teriflunomide phase 3 TEMSO and TOWER studies

2017· article· en· W2596347108 on OpenAlexaff
Mark Freedman, Jerry S. Wolinsky, Ludwig Kappos, Tomas Olsson, Aaron Miller, Karthinathan Thangavelu, Myriam Bénamor, Philippe Truffinet, Paul O’Connor

Bibliographic record

VenueMultiple Sclerosis Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of TorontoOttawa HospitalUniversity of Ottawa
FundersJavna Agencija za Raziskovalno Dejavnost RSSanofi GenzymeIC Design Education CenterBiogen
KeywordsTeriflunomideMedicineMultiple sclerosisPost-hoc analysisSubgroup analysisInternal medicineDiseasePhysical therapyFingolimodConfidence intervalImmunology

Abstract

fetched live from OpenAlex

Teriflunomide is a once-daily oral immunomodulator approved for relapsing-remitting multiple sclerosis (MS). The objective of this post hoc analysis of the phase 3, pooled TEMSO (NCT00134563) and TOWER (NCT00751881) dataset is to evaluate the effect of teriflunomide treatment on annualised relapse rate and disability worsening across patient subgroups defined according to prior disease-modifying therapy exposure. This analysis provides further supportive evidence for a consistent effect of teriflunomide across a broad range of patients with relapsing MS, including patients who have used and discontinued other disease-modifying therapies.

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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.010
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.212
GPT teacher head0.420
Teacher spread0.208 · 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 designMeta-analysis
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".

Quick stats

Citations19
Published2017
Admission routes1
Has abstractyes

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