Preliminary population-based incidence and prevalence estimates of systemic lupus erythematosus: the California Lupus Surveillance Project
Bibliographic record
Abstract
Previous estimates of prevalence and incidence of systemic lupus erythematosus (SLE) in the United States have varied widely due to factors such as heterogeneous source populations, limitations with case ascertainment, and differing case definitions. The California Lupus Surveillance Project (CLSP) is part of a national effort funded by the Centers for Disease Control and Prevention to determine more credible estimates of incidence and prevalence of SLE, with a special focus on Hispanics and Asians. The CLSP is a population-based registry designed to determine the incidence and prevalence of SLE in San Francisco County, CA, USA. Sources of cases included hospitals, rheumatologists, nephrologists, commercial laboratories, and state population databases. These sources were queried for the International Classification of Diseases, Ninth Revision (ICD-9-CM) codes of 710.0 (SLE), 695.4 (discoid lupus), 710.8 (other specified connective tissue disease), and 710.9 (unspecified connective tissue disease). Laboratories were queried for serologic tests including ANA, anti-dsDNA, anti-Smith, antiphospholipid antibodies, and low complement levels. Pathology laboratories were queried for renal and cutaneous biopsies consistent with lupus. Over 15,000 potential SLE patients were identified after the initial queries, and trained abstractors performed detailed medical chart reviews on the >5,500 patients who met the catchment criteria of residence in San Francisco County within the years 2007 to 2009. Cases were defined as patients with documentation of ≥4/11 of the ACR Classification Criteria for SLE. Using SAS 9.3, we calculated prevalence and incidence rates and associated 95% confidence intervals (CIs). Denominators for all rates were obtained from the US Census data (revised 2000 to 2009 intercensal population files) for San Francisco County. The preliminary overall crude prevalence and incidence of SLE in San Francisco County was 90.4/100,000 and 5.1/100,000 respectively. The highest prevalence of disease was observed in Black women (430.6/100,000), followed by Hispanic and Asian (163.8/100,000 and 158.9/100,000, respectively), and White (111.3/100,000) women (Table 1 ). The CLSP uses more complete case finding methods to provide current estimates of prevalence and incidence in a racially and ethnically diverse population. Racial and ethnic disparities in SLE were confirmed with the highest burden of disease in Black women, followed by Hispanic and Asians, and, finally, White women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".