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Record W3049495976 · doi:10.1186/s13075-020-02267-z

Radiographic progression based on baseline characteristics from TNF inhibitor biosimilar studies in patients with rheumatoid arthritis

2020· article· en· W3049495976 on OpenAlexaff
Josef S Smolen, Young Mo Kang, Wan‐Hee Yoo, Paul Emery, Michael E. Weinblatt, Edward Keystone, Mark C. Genovese, Gihyun Myung, In-Young Baek, Jeehoon Ghil

Bibliographic record

VenueArthritis Research & Therapy · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversity of TorontoMount Sinai Hospital
FundersSamsung
KeywordsMedicineBiosimilarRheumatoid arthritisRheumatologyInternal medicineTNF inhibitorEtanerceptInfliximabTumor necrosis factor alphaRadiographyAntirheumatic AgentsBaseline (sea)ArthritisSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: Phase III clinical trials of the tumour necrosis factor inhibitors SB4, SB2, and SB5 (biosimilars to etanercept, infliximab, and adalimumab, respectively) have demonstrated efficacy in moderate-to-severe rheumatoid arthritis (RA). Data from these trials were used to identify baseline characteristics associated with radiographic progression and to build a matrix risk model for its prediction. METHODS: Patients with radiographic progression and baseline demographic and disease characteristic data were pooled across the 3 phase III studies of each biosimilar and its reference product. Baseline demographics and disease characteristics were evaluated for their relationship with radiographic progression (1-year mean change in mTSS > 0); 3 factors were selected based on strongest Pearson's correlation coefficient with the change in modified Total Sharp Score. Univariate logistic regression was performed to assess the association between each baseline factor and the rate of radiographic progression, with subsequent matrix model development performed using multivariate logistic regression. RESULTS: A total of 1371 patients were included in the analysis, with a radiographic progression rate of 27.4%. The 3 baseline predictors of radiographic progression, based on Pearson's correlation coefficient, were 28 swollen joint count (SJC28), C-reactive protein (CRP), and physician global assessment (PhGA). A matrix model showed that the predicted risk of radiographic progression was higher with the increased level of SJC28, CRP, and PhGA (P < 0.001). CONCLUSIONS: In this pooled analysis of phase III clinical trial data of biosimilars for RA, identifiable baseline factors (SJC28, CRP, and PhGA) associated with radiographic progression were similar to those described in prior studies. Even though radiographic progression was minimal, a small number of patients who have increased SJC28, CRP, and PhGA at baseline should be closely monitored and follow treat-to-target approach. CLINICAL TRIAL REGISTRATION NUMBERS: EudraCT 2012-005026-30. Registered 30 April 2013, https://www.clinicaltrialsregister.eu/ctr-search/trial/2012-005026-30/results EudraCT 2012-005733-37. Registered 10 July 2013, https://www.clinicaltrialsregister.eu/ctr-search/trial/2012-005733-37/results EudraCT 2013-005013-13. Registered 01 April 2014, https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-005013-13/results.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.348
Teacher spread0.286 · 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.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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