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Effect of Early Versus Delayed Subcutaneous Interferon (scIFN) β-1a to Achieve No Evidence of Disease Activity (NEDA) in Patients with Clinically Isolated Syndrome (CIS): A Post-hoc Analysis of REFLEXION (P3.111)

2016· article· en· W2551336961 on OpenAlexaff
Patricia K. Coyle, Giancarlo Comi, Mark S. Freedman, Liang Chen, Kurt Marhardt, Ludwig Kappos

Bibliographic record

VenueNeurology · 2016
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsPost-hoc analysisMedicineDiseasePost hocInternal medicinePediatrics

Abstract

fetched live from OpenAlex

Objective: To investigate early versus delayed scIFNβ-1a treatment in achieving NEDA, an absence of clinical and magnetic resonance imaging (MRI) disease activity, up to 5 years post-randomization. Background: Early treatment with scIFNβ-1a 44µg three-times-weekly (tiw) or once-weekly (qw) after CIS significantly delays development of clinical and MRI disease activity for up to 5 years. Methods: In REFLEX, patients with CIS were randomized to double-blind scIFNβ-1a 44µg tiw, qw, or placebo for 24 months (upon clinically-definite multiple sclerosis [MS], patients switched to open-label scIFNβ-1a tiw); in REFLEXION, placebo patients switched to tiw (delayed treatment [DT]); scIFNβ-1a patients continued their initial qw/tiw regimen for up to 60-months post-randomization. This post-hoc analysis was conducted in the integrated intent-to-treat REFLEX plus REFLEXION population (tiw, n=171; qw, n=175; DT, n=171). Results: NEDA was achieved in greater proportions of patients on tiw (up to Year 1, 35.1[percnt]; Year 2, 20.5[percnt]; Year 3, 15.2[percnt]; Year 4, 12.3[percnt]; Year 5, 7.0[percnt]) than qw (24.6[percnt]; 15.4[percnt]; 7.4[percnt]; 5.7[percnt]; 2.3[percnt]) and DT (14.6[percnt]; 5.9[percnt]; 4.7[percnt]; 2.9[percnt]; 0.6[percnt]). In patients with NEDA up to 2 years, the proportion who remained NEDA up to 5 years was higher with early scIFNβ-1a tiw (34.3[percnt]) versus qw (14.8[percnt]) and DT (10.0[percnt]). In true CIS patients (not meeting McDonald 2010 criteria for relapsing-remitting [RR] MS), higher proportions achieved NEDA up to each timepoint with scIFNβ-1a tiw and qw versus DT (Year 5: tiw, 8.3[percnt]; qw, 2.8[percnt]; DT, 1.0[percnt]). Similar results were observed for patients meeting McDonald 2010 criteria for RRMS (n=195). Conclusions: Early treatment with scIFNβ-1a resulted in more patients achieving NEDA versus DT, maintained up to 5 years post-randomization, with a pronounced effect for more frequent dosing (tiw versus qw), and a clear benefit for treating CIS, including patients meeting the contemporary definition. Study supported by: Merck KGaA, Germany

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Non-randomized triallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized triallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.293
Teacher spread0.284 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNon-randomized trial · Randomized trial
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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