Developing and Refining New Candidate Criteria for Systemic Lupus Erythematosus Classification: An International Collaboration
Bibliographic record
Abstract
OBJECTIVE: To define candidate criteria within multiphase development of systemic lupus erythematosus (SLE) classification criteria, jointly supported by the American College of Rheumatology and the European League Against Rheumatism. Prior steps included item generation and reduction by Delphi exercise, further narrowed to 21 items in a nominal group technique exercise. Our objectives were to apply an evidence-based approach to the 21 candidate criteria, and to develop hierarchical organization of criteria within domains. METHODS: A literature review identified the sensitivity and specificity of the 21 candidate criteria. Data on the performance of antinuclear antibody (ANA) as an entry criterion and operating characteristics of the candidate criteria in early SLE patients were evaluated. Candidate criteria were hierarchically organized into clinical and immunologic domains, and definitions were refined in an iterative process. RESULTS: Based on the data, consensus was reached to use a positive ANA of ≥1:80 titer (HEp-2 cells immunofluorescence) as an entry criterion and to have 7 clinical and 3 immunologic domains, with hierarchical organization of criteria within domains. Definitions of the candidate criteria were specified. CONCLUSION: Using a data-driven process, consensus was reached on new, refined criteria definitions and organization based on operating characteristics. This work will be followed by a multicriteria decision analysis exercise to weight criteria and to identify a threshold score for classification on a continuous probability scale.
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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 | 0.054 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".