Cardiovascular Disease Disparities in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Disparities in health outcomes are one of the greatest healthcare challenges of our times. Disparities in systemic lupus erythematosus have been reported since the 1970s, when a study performed in Alabama showed the increased frequency of SLE in people of African descent.1 Numerous subsequent studies have reported that Black and other minority populations have worse morbidity and mortality from SLE compared to White individuals.2 Cardiovascular disease (CVD) is the leading cause of death in people with SLE3; however, CVD among Black patients with SLE is understudied.4 Garg and colleagues sought to address this knowledge gap in this issue of The Journal of Rheumatology .5 To do so, they used a population-based cohort derived from the Georgia Lupus Registry (GLR), one of the Centers for Disease Control and Prevention (CDC) SLE registries.6-10 The GLR includes incident SLE cases from 2002 to 2004 from Fulton and DeKalb counties, which include the city of Atlanta, Georgia, in the United States.11 These cases were painstakingly validated through manual chart reviews; all included cases either met the American College of Rheumatology (ACR) 1997 SLE criteria or had 3 ACR criteria and had SLE documented by a rheumatologist. The investigators ascertained hospitalizations and death through the Georgia Hospital Discharge Database and the National Death Index. Their cohort included 336 patients with incident SLE, of which 75% were Black. The authors followed these patients for 15 years: 2 years before diagnosis and 13 years after diagnosis. … Address correspondence to Dr. A. Duarte-García, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. Email: duarte.ali{at}mayo.edu.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".