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Record W1999913066 · doi:10.1055/s-0033-1343795

Teriflunomide for the Treatment of Multiple Sclerosis

2013· review· en· W1999913066 on OpenAlexaff
Jiwon Oh, Paul O’Connor

Bibliographic record

VenueSeminars in Neurology · 2013
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFungal Plant Pathogen Control
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTeriflunomideMedicineMultiple sclerosisTolerabilityContext (archaeology)Clinical trialAlternative medicineIntensive care medicineMedical physicsAdverse effectPharmacologyInternal medicinePathologyImmunologyFingolimod

Abstract

fetched live from OpenAlex

Several novel oral agents are emerging for use in multiple sclerosis (MS). Among these oral agents, teriflunomide is showing promise with respect to clinical efficacy and safety in relapsing MS patients. In this review, the authors clarify the role of teriflunomide in the context of current and emerging MS treatment options by summarizing salient points on the use of teriflunomide in MS, with a discussion of teriflunomide's development, pharmacologic properties, preclinical and clinical trials, and safety and tolerability.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.084
GPT teacher head0.263
Teacher spread0.179 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2013
Admission routes1
Has abstractyes

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