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Record W4376563614 · doi:10.1093/rheumatology/keu102

Rheumatoid arthritis: clinical features

2014· article· en· W4376563614 on OpenAlexaff
Andrew ̈sto, E G Chelliah, Theodoros Dimitroulas, Margaret-Mary Gordon, Nicola Hewson, Justine Mitchell, Kevin Dinnell, Senam Beckley-Kartey, Hok Pang, José Saraiva-Ribeiro, Edward Keystone, Mark C. Genovese, Stephen Hall, Pedro C. Miranda, Sang‐Cheol Bae, Chenglong Han, T. Gathany, Yiying Zhou, Stephen Xu, Elizabeth C. Hsia, Geetha Lakshmi Janakiraman, Clive Kelly, Mohamed Nisar, Subha Arthanari, Felix Woodhead, Alec Price-Forbes, David Middleton, Owen Dempsey, Julie Dawson, Nav Sathi, Yasmeen Ahmad, Gouri Koduri, Adam Young

Bibliographic record

VenueLara D. Veeken · 2014
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsRheumatoid arthritisMedicineArthritisDermatologyInternal medicine

Abstract

fetched live from OpenAlex

Background: NICE guidelines recommend that patients with severe active RA be treated with a biologic disease-modifying anti-rheumatic drug as monotherapy (bDMARD mono) if they respond inadequately to 2 traditional DMARDs and are intolerant to MTX.Registry and healthcare utilization data have shown that around one-third of RA patients receive their biologic treatment as monotherapy.Methods: This UK wide chart review was undertaken to better understand the current management of RA patients with biologics in monotherapy, in particular, why patients receive monotherapy and how effective this is in daily practice.The chart review was conducted across 26 rheumatology departments between September 2012 and May 2013 and data from 309 RA patients, who had been prescribed a bDMARD mono, were collected retrospectively.Data are presented from an interim analysis of 150 patients.Results: The mean patient age was 62.4 years and mean duration of disease was 16.3 years.The majority of patients were female (109).102 (74.4%) patients were RF positive (n ¼ 137) and 34 (36.6%)anticyclic citrullinated peptide (anti-CCP) antibody positive (n ¼ 93).With regards to past RA treatment (prior to switch to monotherapy, n ¼ 61), the most frequently used bDMARDs monotherapies (at any time prior to switch) were etanercept (ETN; 19.3%), adalimumab (ADA; 14%), certolizumab pegol (CZP, 7.3%), rituximab (RTX, 4.7%) and tocilizumab (TCZ, 2.7%).55 (36.7%) patients had used two or more bDMARD monotherapies.The most frequently used traditional DMARDs (n ¼ 146) were MTX (92%), SSZ (74.7%) and HCQ (42.7%).With regards to current RA treatment (all patients receiving bDMARD mono), the most frequently used bDMARDs were ETN (42.7%),ADA (26%), TCZ (10.7%),RTX (9.3%) and CZP (8%).The main reason for prescribing bDMARD monotherapy (n ¼ 150) was unknown (88%, assumed clinician decision), with other stated reasons including patient preference (6.7%) and contra-indications (7.3%).Nearly a quarter of patients were taking a non-NICE approved monotherapy drug and only 24.2% were in DAS28 ESR remission.Conclusion: Registry and healthcare utilization data have shown that around one-third of RA patients receive their biologic treatment as monotherapy.The results of this audit highlight a patient population where treatment outcomes with bDMARD monotherapy are still not optimal (only 24.2% were in DAS28 ESR remission).76.7% of the patients were treated with NICE approved TNF inhibitors, and 55 (36.7%) patients had used two or more bDMARD monotherapies, indicating that there are a significant number of patients where combination therapy with traditional DMARDs is not an option.As remission is the treatment goal and only 24.2% of patients were in remission, there appears to be an unmet medical need.Alternative treatment options should be explored to ensure better treatment outcomes for patients with this debilitating disease.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.005

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 head0.019
GPT teacher head0.313
Teacher spread0.294 · 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 designNot applicable
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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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