Evaluation of the <scp>EULAR</scp>/American College of Rheumatology Classification Criteria for Systemic Lupus Erythematosus in a <scp>Population‐Based</scp> Registry
Bibliographic record
Abstract
OBJECTIVE: Using the Manhattan Lupus Surveillance Program, a multiracial/ethnic population-based registry, we aimed to compare 3 commonly used classification criteria for systemic lupus erythematosus (SLE) to identify unique cases and determine the incidence and prevalence of SLE using the EULAR/American College of Rheumatology (ACR) criteria. METHODS: SLE cases were defined as fulfilling the 1997 ACR, the Systemic Lupus International Collaborating Clinics (SLICC), or the EULAR/ACR classification criteria. We quantified the number of cases uniquely associated with each and the number fulfilling all 3 criteria. Prevalence and incidence using the EULAR/ACR classification criteria and associated 95% confidence intervals (95% CIs) were calculated. RESULTS: A total of 1,497 cases fulfilled at least 1 of the 3 classification criteria, with 1,008 (67.3%) meeting all 3 classifications, 138 (9.2%) fulfilling only the SLICC criteria, 35 (2.3%) fulfilling only the 1997 ACR criteria, and 34 (2.3%) uniquely fulfilling the EULAR/ACR criteria. Patients solely satisfying the EULAR/ACR criteria had <4 manifestations. The majority classified only by the 1997 ACR criteria did not meet any of the defined immunologic criteria. Patients fulfilling only the SLICC criteria did so based on the presence of features unique to this system. Using the EULAR/ACR classification criteria, age-adjusted overall prevalence and incidence rates of SLE in Manhattan were 59.6 (95% CI 55.9-63.4) and 4.9 (95% CI 4.3-5.5) per 100,000 population, with age-adjusted prevalence and incidence rates highest among non-Hispanic Black female patients. CONCLUSION: Applying the 3 commonly used classification criteria to a population-based registry identified patients with SLE fulfilling only 1 validated definition. The most recently developed EULAR/ACR classification criteria revealed prevalence and incidence estimates similar to those previously established for the ACR and SLICC classification schemes.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".