Environmental Risk Factors for Systemic Lupus Erythematosus Through the Lens of Social Determinants of Health
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) is a serious multisystem autoimmune disease, marked by alarming sociodemographic inequities. In the United States and around the world, social disadvantage is strongly tied to higher prevalence, more severe disease, and poorer outcomes. A growing list of environmental exposures that contribute to the risk and incidence of SLE have been investigated, and many are now established. However, these environmental exposures-including exposure to air pollution and other contaminants, lifestyle and behavioral factors, and psychologic stress and distress-are not evenly distributed in any population. Individuals of lower socioeconomic status and historically minoritized groups suffer from an imbalanced burden of adverse environmental exposures. In research, clinical practice, and policy making, the strong association of social determinants of health (SDoH) with these exposures has not been given adequate spotlight. In this narrative review, we examine known associations between environmental exposures and SLE risk through the lens of SDoH, laying the foundation for future research and policies to target the environmental risk factors for SLE with awareness of the populations disproportionately affected and the contributing SDoH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".