Contribution of Socioeconomic Status to Racial/Ethnic Disparities in Adverse Pregnancy Outcomes Among Women With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: We examined rates of adverse pregnancy outcomes (APO) by race/ethnicity among women with systemic lupus erythematosus (SLE), with and without antiphospholipid antibodies (aPL), and whether socioeconomic status (SES) accounted for differences. METHODS: Data were from the PROMISSE (Predictors of Pregnancy Outcome: Biomarkers in Antiphospholipid Antibody Syndrome and Systemic Lupus Erythematosus) study, a multicenter study that enrolled 346 patients with SLE and 62 patients with SLE and aPL (50% white, 20% African American, 17% Hispanic, 12% Asian/Pacific Islander). Measures of SES were educational attainment, median community income, and community education. Logistic regression analyses were conducted to determine odds of APO for each racial/ethnic group, controlling first for age and clinical variables, and then for SES. RESULTS: The frequency of APO in white women with SLE, with and without aPL, was 29% and 11%, respectively. For African American and Hispanic women it was approximately 2-fold greater. In African American women with SLE alone, adjustment for clinical variables attenuated the odds ratio (OR) from 2.7 (95% confidence interval [95% CI] 1.3-5.5) to 2.3 (95% CI 1.1-5.1), and after additional adjustment for SES, there were no longer significant differences in APO compared to whites. In contrast, in SLE patients with aPL, whites, African Americans, and Hispanics had markedly higher risks of APO compared to white SLE patients without aPL (OR 3.5 [95% CI 1.4-7.7], OR 12.4 [95% CI 1.9-79.8], and OR 10.4 [95% CI 2.5-42.4], respectively), which were not accounted for by clinical or SES covariates. CONCLUSION: This finding suggests that for African American women with SLE without aPL, SES factors are key contributors to disparities in APO, despite monthly care from experts, whereas other factors contribute to disparities in SLE with aPL.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".