SLE classification criteria item relationships: implications on SLE as a disease entity
Bibliographic record
Abstract
OBJECTIVES: This study aims to analyse potential relationships between European Alliance of Associations for Rheumatology (EULAR)/American College for Rheumatology (ACR) classification criteria domains and individual criteria items in a large systemic lupus erythematosus (SLE) patient cohort. Previous findings showed meaningful associations only within organ systems, but not across them. We seek to validate these findings and expand on them. METHODS: Cluster analysis was performed on the EULAR/ACR criteria domains in a cohort of 1196 patients with SLE. Criteria items were analysed as binary variables (ever present = 1, always absent = 0) and tested for associations using network analysis. RESULTS: The cluster analysis resulted in 10 clusters, but with no convincing patterns beyond antibody-organ relationships. Relevant correlations between items were found within the domains, but some associations between items of different domains still showed significant, if mostly weak associations with r values of 0.10 to 0.26. These included correlations between antibodies to double-stranded DNA and Sm, low complements and lupus nephritis, and between antiphospholipid antibodies and thrombocytopenia. Anti-Sm antibodies were also associated with alopecia and leukopenia, autoimmune haemolysis with seizures, and serositis with fever. Joint involvement was negatively correlated with lupus nephritis and thrombocytopenia. The network analysis showed fever and serositis detached from the other items, with items within organ domains grouped. CONCLUSIONS: This comprehensive analysis of relationships between the domains and items of the EULAR/ACR SLE classification criteria underlines the relevance of the domain structure. Overall, the data are more compatible with chance distribution than with fixed subsets of SLE.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".