Use of SLICC criteria in a large, diverse lupus registry enables SLE classification of a subset of ACR-designated subjects with incomplete lupus
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".