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EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2019 update

2020· article· en· W4231972505 on OpenAlexaff
Josef S Smolen, Robert Landewé, J. W. J. Bijlsma, Gerd R Burmester, Maxime Dougados, Andreas Kerschbaumer, Iain B. McInnes, Alexandre Sepriano, Ronald van Vollenhoven, Maarten de Wit, Daniel Aletaha, Martin Aringer, Johan Askling, Alejandro Balsa, Maarten Boers, Alfons A den Broeder, Maya H Buch, Frank Buttgereit, Roberto Caporali, Myrna Cardiel, Diederik De Cock, Cătălin Codreanu, Maurizio Cutolo, Christopher J Edwards, Yvonne van Eijk‐Hustings, Paul Emery, Axel Finckh, Laure Gossec, Jacques‐Eric Gottenberg, Merete Lund Hetland, T. Huizinga, Marios Koloumas, Zhanguo Li, Xavier Mariette, Ulf Müller‐Ladner, Eduardo Mysler, José António Pereira da Silva, Gyula Poór, Janet Pope, Andrea Rubbert‐Roth, Adeline Ruyssen‐Witrand, Kenneth G. Saag, Anja Strangfeld, Tsutomu Takeuchi, Marieke Voshaar, René Westhovens, Désirée van der Heijde

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsWestern University
FundersEuropean League Against Rheumatism
KeywordsMedicineAntirheumatic drugsRheumatoid arthritisAntirheumatic AgentsDisease managementIntensive care medicineArthritisDiseaseBiological drugsPhysical therapyInternal 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)0.032
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: Methods · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0050.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0100.010

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 head0.053
GPT teacher head0.311
Teacher spread0.258 · 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
GenreMethods

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

Citations2,766
Published2020
Admission routes1
Has abstractno

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