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Record W2553060585 · doi:10.1177/2055217316676644

Improvement in relapse recovery with peginterferon beta-1a in patients with multiple sclerosis

2016· article· en· W2553060585 on OpenAlexaff
Thomas F. Scott, Bernd C. Kieseier, Scott D. Newsome, Douglas L. Arnold, Xiaojun You, Serena Hung, Bjoern Sperling

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineMultiple sclerosisInternal medicinePlaceboPost-hoc analysisGastroenterologyPsychiatryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Subcutaneous peginterferon beta-1a every 2 weeks significantly affects clinical outcomes in patients with relapsing-remitting multiple sclerosis (RRMS). OBJECTIVES: To explore relationships between relapses and worsening of disability in patients with RRMS, and assess the treatment effect of peginterferon beta-1a on relapse recovery. METHODS: Post-hoc analysis of the 2-year, randomized, double-blind, parallel-group, Phase 3 ADVANCE study. The severity of relapses, proportion of patients with relapses associated with residual disability (onset of 24-week confirmed disability progression (CDP) within 90 days following a relapse), and persistence of changes in Functional Systems Scores, were compared between treatment groups. RESULTS: = 0.02). Reduction in relapses associated with residual disability was greater than the treatment effect on overall relapse rate, and occurred despite similar relapse severity across treatment groups. CONCLUSIONS: The beneficial effect of peginterferon beta-1a on risk of CDP may be attributable to the combination of an overall reduction in the risk of relapses and improvement in recovery from relapses, thus limiting further disability progression. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00906399.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.098
GPT teacher head0.322
Teacher spread0.224 · 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 teacher head, not a consensus.

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

Citations12
Published2016
Admission routes1
Has abstractyes

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