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Clinical Efficacy of BG-12 (Dimethyl Fumarate) for Relapsing–Remitting Multiple Sclerosis (RRMS) According to Prior Therapy: An Integrated Analysis of the Phase 3 DEFINE and CONFIRM Studies (P07.128)

2013· article· en· W207924865 on OpenAlexaff
Michael Hutchinson, Ralf Gold, Robert J. Fox, Eva Havrdová, Gavin Giovannoni, Annie Zhang, Christophe Hotermans, M. Stephan, Katherine Dawson, Amit Bar‐Or

Bibliographic record

VenueNeurology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsRelapsing remittingDimethyl fumarateMultiple sclerosisMedicineInternal medicineOncologyImmunology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the clinical efficacy of BG-12 (dimethyl fumarate) in an integrated analysis of DEFINE and CONFIRM according to prior treatment with one, two or more, or any number of approved interferon β-1a/b (IFN) or glatiramer acetate (GA) treatments (ABCR) (post-hoc analyses).

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)0.005
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.102
GPT teacher head0.382
Teacher spread0.280 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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