Evaluating the Threshold Score for Classification of Systemic Lupus Erythematosus Using the EULAR/ACR Criteria
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether a change in the European Alliance of Associations for Rheumatology (EULAR)/American College of Rheumatology (ACR) systemic lupus erythematosus (SLE) classification criteria threshold score affects accurate classification of SLE cases compared to disease-based control subjects. We evaluated a range of threshold scores to determine the score that maximizes the accurate classification of early SLE. METHODS: We conducted a cross-sectional study comparing SLE cases and control patients. A EULAR/ACR criteria score was calculated using baseline information. Sensitivity, specificity, positive likelihood ratios (+LRs), and negative likelihood ratios (-LRs) with 95% CIs were used to evaluate operating characteristics. Threshold scores of 6 to 12 were evaluated in subjects with early disease (ie, disease duration of ≤ 5 years). +LRs > 10 and -LRs < 0.1 provide evidence to rule in or rule out SLE. RESULTS: A total of 2764 patients were included: 1980 SLE cases who fulfilled either the ACR or Systemic Lupus International Collaborating Clinics criteria and 784 control subjects. The EULAR/ACR SLE criteria had a sensitivity of 98% (95% CI 97-98), a specificity of 99% (95% CI 98-100), a +LR of 95.5 (95% CI 48.0-190), and a -LR 0.03 (95% CI 0.02-0.03). The criteria operated well in those with early disease, in women, in men, and in White, Black, Chinese, and Filipino people. A score of 10 maximized the accurate classification of patients with early disease (+LR 174.4, 95% CI 43.8-694.6; -LR 0.03, 95% CI 0.02-0.04). An increase in the threshold score from 10 to 11 resulted in significant worsening in the -LR (threshold score 10: -LR 0.03, 95% CI 0.02-0.03 vs threshold score 11: -LR 0.05, 95% CI 0.04-0.06). CONCLUSION: The EULAR/ACR SLE classification criteria threshold score of 10 performs well, particularly among those with early disease and across sexes and ethnicities.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".