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Predictive Value of Early MRI Measures for Long-Term Disease Activity in Patients with Relapsing-Remitting Multiple Sclerosis Receiving IFN β-1a SC tiw or IFN β-1a IM qw: Post Hoc Analyses of the EVIDENCE Study (P6.189)

2016· article· en· W2774316186 on OpenAlexaff
Patricia K. Coyle, Mark S. Freedman, Fernando Dangond, Juanzhi Fang, Anthony T. Reder

Bibliographic record

VenueNeurology · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMultiple sclerosisRelapsing remittingPost-hoc analysisMedicinePredictive valueDiseasePost hocTerm (time)Value (mathematics)Internal medicineGastroenterologyOncologyPathologyImmunologyStatisticsMathematicsPhysics

Abstract

fetched live from OpenAlex

Objective: Examine early MRI differences between interferon beta-1a (IFN β-1a) treatments and the relationship of baseline lesions to subsequent no evidence of disease activity (NEDA) status. Background: In EVIDENCE, patients with RRMS were randomized to IFN β-1a 44 µg subcutaneously (SC) 3x/week (n=339) or IFN β-1a 30 µg intramuscularly (IM) 1x/week (n=338). Methods: Post hoc analyses assessed whether baseline (Week [W] 0) lesions predicted NEDA up to W48 (no relapses, no disability progression [≥1-point increase in EDSS score sustained for 12 weeks], and no active T2 lesions up to W48 [7 MRI scans]) and W72 (no relapses, no disability progression, and no active T2 lesions up to W72 [8 MRI scans]). Results: IFN β-1a SC was associated with fewer mean Gd+ and active T2 lesions/patient/scan by W8 and W12, respectively, versus IFN β-1a IM (Gd+ 0.79 vs 1.34, p=0.002; T2 0.42 vs 0.55, p=0.008). More IFN β-1a SC patients achieved NEDA up to W48 (33.2[percnt] vs 18.9[percnt], p<0.001) and W72 (26.4[percnt] vs 11.1[percnt], p<0.001) versus IFN β-1a IM. Baseline Gd+ lesions did not differ significantly between groups; presence (vs absence) predicted NEDA status up to W72 for IFN β-1a IM (2.8[percnt] vs 19.4[percnt], p=0.022), but not for IFN β-1a SC (16.9[percnt] vs 36.3[percnt], p=0.187). More patients with and without baseline Gd+ lesions achieved NEDA up to W48 (p≤0.017) and W72 (p≤0.003) with IFN β-1a SC versus IFN β-1a IM. Conclusions: IFN β-1a SC demonstrated early MRI benefits and was associated with more patients (either with or without baseline Gd+ lesions) achieving NEDA, versus IFN β-1a IM. For IFN β-1a IM, baseline Gd+ lesions were associated with reduced likelihood of having NEDA. Study supported by: EMD Serono, Inc., Rockland, MA, USA (a business of Merck KGaA, Darmstadt, Germany); Pfizer Inc, New York, NY, USA.

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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.289
Teacher spread0.199 · 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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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