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Record W4308769923 · doi:10.1136/ard-2022-223356

EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2022 update

2022· article· en· W4308769923 on OpenAlexaff
Josef S Smolen, Robert Landewé, Sytske Anne Bergstra, Andreas Kerschbaumer, Alexandre Sepriano, Daniel Aletaha, Roberto Caporali, Christopher J Edwards, Kimme L Hyrich, Janet Pope, Savia de Souza, Tanja Stamm, Tsutomu Takeuchi, Patrick Verschueren, Kevin Winthrop, Alejandro Balsa, Joan M. Bathon, Maya H Buch, Gerd R Burmester, Frank Buttgereit, Myrna Cardiel, Katerina Chatzidionysiou, Cătălin Codreanu, Maurizio Cutolo, Alfons A den Broeder, Khadija El Aoufy, Axel Finckh, João Eurico Fonseca, Jacques‐Eric Gottenberg, Espen A. Haavardsholm, Annamaria Iagnocco, Kim Lauper, Zhanguo Li, Iain B. McInnes, Eduardo Mysler, Peter Nash, Gyula Poór, Gorica Ristić, Felice Rivellese, Andrea Rubbert‐Roth, Hendrik Schulze‐Koops, Nikolay Stoilov, Anja Strangfeld, Annette H M van der Helm–van Mil, Elsa van Duuren, T. P. M. Vliet Vlieland, René Westhovens, Désirée van der Heijde

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsWestern University
FundersMedacNational Institute for Health and Care ResearchEuropean League Against RheumatismBiogenGilead SciencesSanofiAmgenPfizerEli Lilly and Company
KeywordsMedicineAntirheumatic drugsRheumatoid arthritisAntirheumatic AgentsArthritisHydroxychloroquineDiseaseBiological drugsIntensive care medicineImmunologyInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.016
metaresearch head score (Gemma)0.029
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0050.003
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0120.012

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 head0.041
GPT teacher head0.308
Teacher spread0.267 · 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".

Quick stats

Citations1,788
Published2022
Admission routes1
Has abstractno

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