Trajectory of Damage Accrual in Systemic Lupus Erythematosus Based on Ethnicity and Socioeconomic Factors
Bibliographic record
Abstract
OBJECTIVE: The Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) is associated with increased healthcare costs and mortality. We compared the trajectory of total and individual damage items of the SDI in African American vs White ethnicities in a large prospective systemic lupus erythematosus (SLE) cohort. We also estimated the association between ethnicity and individual damage items after adjusting for several socioeconomic factors. METHODS: Poisson regression was used to calculate the rate of damage per year for each organ. Cox regression modeling was used to determine the association between time to the individual damage item and ethnicity. RESULTS: We included 2436 patients: 42.9% African American, 57.1% White, and 92% female. There was a linear relationship between time since diagnosis and mean SDI score, with no plateau. Compared to White patients, African American patients had a faster total, renal, pulmonary, and skin damage accrual rate even after adjustment for differences in socioeconomic variables. CONCLUSION: The linear increase in damage in both ethnicities over time is of particular concern. African American patients accrued more damage at a faster rate compared to White patients. For a few organs, higher rates of damage in African American patients was partially explained by socioeconomic differences, whereas for most organs, the difference persisted after adjustment for these factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".