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Record W2596603956 · doi:10.1136/lupus-2016-000176

Use of SLICC criteria in a large, diverse lupus registry enables SLE classification of a subset of ACR-designated subjects with incomplete lupus

2017· article· en· W2596603956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLupus Science & Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Human Genome Research InstituteGenentechNational Institutes of HealthHorizon PharmaceuticalsNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteMallinckrodt PharmaceuticalsU.S. Department of Veterans Affairs
KeywordsMedicineSystemic lupus erythematosusAutoantibodyInternal medicineMedical recordImmunologyAntibodyDisease

Abstract

fetched live from OpenAlex

Objective SLE is traditionally classified using the American College of Rheumatology (ACR) criteria. The Systemic Lupus International Collaborating Clinics (SLICC) recently validated an alternative system. This study examined large cohorts of subjects with SLE and incomplete lupus erythematosus (ILE) to compare the impact of ACR and SLICC criteria. Methods Medical records of subjects in the Lupus Family Registry and Repository were reviewed for documentation of 1997 ACR classification criteria, SLICC classification criteria and medication usage. Autoantibodies were assessed by indirect immunofluorescence (ANA, antidouble-stranded DNA), precipitin (Sm) and ELISA (anticardiolipin). Other relevant autoantibodies were detected by precipitin and with a bead-based multiplex assay. Results Of 3575 subjects classified with SLE under at least one system, 3312 (92.6%) were classified as SLE by both systems (SLE both ), 85 only by ACR criteria (SLE ACR-only ) and 178 only by SLICC criteria (SLE SLICC-only ). Of 440 subjects meeting 3 ACR criteria, 33.9% (149/440) were SLE SLICC-only , while 66.1% (n=291, designated ILE) did not meet the SLICC classification criteria. Under the SLICC system, the complement criterion and the individual autoantibody criteria enabled SLE classification of SLE SLICC-only subjects, while SLE ACR-only subjects failed to meet SLICC classification due to the combined acute/subacute cutaneous criterion. The SLICC criteria classified more African-American subjects by the leucopenia/lymphopenia criterion than did ACR criteria. Compared with SLE ACR-only subjects, SLE SLICC-only subjects exhibited similar numbers of affected organ systems, rates of major organ system involvement (∼30%: pulmonary, cardiovascular, renal, neurological) and medication history. Conclusions The SLICC criteria classify more subjects with SLE than ACR criteria; however, individuals with incomplete lupus still exist under SLICC criteria. Subjects who gain SLE classification through SLICC criteria exhibit heterogeneous disease, including potential major organ involvement. These results provide supportive evidence that SLICC criteria may be more inclusive of SLE subjects for clinical studies.

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.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.004
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.102
GPT teacher head0.361
Teacher spread0.259 · 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