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Record W2965565685 · doi:10.1002/art.40930

2019 European League Against Rheumatism/American College of Rheumatology Classification Criteria for Systemic Lupus Erythematosus

2019· article· en· W2965565685 on OpenAlexaff
Martin Aringer, Karen H. Costenbader, David Daikh, Ralph Brinks, Marta Mosca, Rosalind Ramsey‐Goldman, Josef S Smolen, David Wofsy, Dimitrios T. Boumpas, Diane L. Kamen, David Jayne, Ricard Cervera, N. Costedoat‐Chalumeau, Betty Diamond, Dafna D. Gladman, Bevra Hahn, Falk Hiepe, Søren Jacobsen, Dinesh Khanna, Kirsten Lerstrøm, Elena Massarotti, Joseph M. McCune, Guillermo Ruiz‐Irastorza, Jorge Sánchez‐Guerrero, Matthias Schneider, Murray B. Urowitz, George Βertsias, Bimba F. Hoyer, Nicolai Leuchten, Chiara Tani, Sara K. Tedeschi, Zahi Touma, Gabriela Schmajuk, Branimir Anić, Florence Assan, Tak Mao Chan, Ann E. Clarke, Mary K. Crow, László Czirják, Andrea Doria, W. Graninger, Bernadett Halda-Kiss, Sarfaraz Hasni, Peter Izmirly, Michelle Jung, Gabór Kumánovics, Xavier Mariette, Ivan Padjen, José María Pego‐Reigosa, Juanita Romero‐Díaz, Íñigo Rúa‐Figueroa Fernández, Raphaèle Séror, Georg Stummvoll, Yoshiya Tanaka, Maria G. Tektonidou, Carlos Vasconcelos, Edward M Vital, Daniel J. Wallace, Şule Yavuz, Pier Luigi Meroni, Marvin J. Fritzler, Ray Naden, Thomas Dörner, Sindhu R. Johnson

Bibliographic record

VenueArthritis & Rheumatology · 2019
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsMcMaster UniversityUniversity of CalgaryUniversity of TorontoUniversity Health NetworkMount Sinai HospitalToronto Western Hospital
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthEuropean League Against RheumatismAcademy of Medical SciencesMedical Research CouncilIntramural Research ProgramNational Institute for Health and Care Research
KeywordsRheumatismRheumatologyLeagueMedicineInternal medicineDermatology

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop new classification criteria for systemic lupus erythematosus (SLE) jointly supported by the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). METHODS: This international initiative had four phases. 1) Evaluation of antinuclear antibody (ANA) as an entry criterion through systematic review and meta-regression of the literature and criteria generation through an international Delphi exercise, an early patient cohort, and a patient survey. 2) Criteria reduction by Delphi and nominal group technique exercises. 3) Criteria definition and weighting based on criterion performance and on results of a multi-criteria decision analysis. 4) Refinement of weights and threshold scores in a new derivation cohort of 1,001 subjects and validation compared with previous criteria in a new validation cohort of 1,270 subjects. RESULTS: The 2019 EULAR/ACR classification criteria for SLE include positive ANA at least once as obligatory entry criterion; followed by additive weighted criteria grouped in 7 clinical (constitutional, hematologic, neuropsychiatric, mucocutaneous, serosal, musculoskeletal, renal) and 3 immunologic (antiphospholipid antibodies, complement proteins, SLE-specific antibodies) domains, and weighted from 2 to 10. Patients accumulating ≥10 points are classified. In the validation cohort, the new criteria had a sensitivity of 96.1% and specificity of 93.4%, compared with 82.8% sensitivity and 93.4% specificity of the ACR 1997 and 96.7% sensitivity and 83.7% specificity of the Systemic Lupus International Collaborating Clinics 2012 criteria. CONCLUSION: These new classification criteria were developed using rigorous methodology with multidisciplinary and international input, and have excellent sensitivity and specificity. Use of ANA entry criterion, hierarchically clustered, and weighted criteria reflects current thinking about SLE and provides an improved foundation for SLE research.

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)0.010
metaresearch head score (Gemma)0.019
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 score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.004

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 head0.021
GPT teacher head0.291
Teacher spread0.270 · 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

Citations2,515
Published2019
Admission routes1
Has abstractyes

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