Use of Consensus Methodology to Determine Candidate Items for Systemic Lupus Erythematosus Classification Criteria
Bibliographic record
Abstract
OBJECTIVE: Given the complexity and heterogeneity of systemic lupus erythematosus (SLE), high-performing classification criteria are critical to advancing research and clinical care. A collaborative effort by the European League Against Rheumatism and the American College of Rheumatology was undertaken to generate candidate criteria, and then to reduce them to a smaller set. The objective of the current study was to select a set of criteria that maximizes the likelihood of accurate classification of SLE, particularly early disease. METHODS: An independent panel of international SLE experts and the SLE classification criteria steering committee (conducting SLE research in Canada, Mexico, United States, Austria, Germany, Greece, France, Italy, and Spain) ranked 43 candidate criteria. A consensus meeting using nominal group technique (NGT) was conducted to reduce the list of criteria for consideration. RESULTS: The expert panel NGT exercise reduced the candidate criteria for SLE classification from 43 to 21. The panel distinguished potential "entry criteria," which would be required for classification, from potential "additive criteria." Potential entry criteria were antinuclear antibody (ANA) ≥ 1:80 (HEp-2 immunofluorescence), and low C3 and/or low C4. The use of low complement as an entry criterion was considered potentially useful in cases with negative ANA. Potential additive criteria included lupus nephritis by renal biopsy, autoantibodies, cytopenias, acute and chronic cutaneous lupus, alopecia, arthritis, serositis, oral mucosal lesions, central nervous system manifestations, and fever. CONCLUSION: The NGT exercise resulted in 21 candidate SLE classification criteria. The next phases of SLE classification criteria development will require refinement of criteria definitions, evaluation of the ability to cluster criteria into domains, and evaluation of weighting of criteria.
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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 | 0.106 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".