Socioeconomic consequences of systemic lupus erythematosus
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review addresses recent literature investigating the socioeconomic consequences of systemic lupus erythematosus (SLE). We highlight the latest updates on health disparities affecting the SLE population, the direct and indirect economic costs of the disease, and less quantifiable costs such as reduced health-related quality of life (HRQoL). RECENT FINDINGS: Health disparities continue to exist among socially disadvantaged populations, including African Americans, Hispanics, and patients with decreased educational attainment and in poverty. Direct and indirect costs are substantial. Recent work provides updated cost estimates for patients with SLE outside of North America, including those in developing countries. Previous research has largely focused on costs of the general SLE population and those with renal manifestations or active SLE, whereas recent research addresses special populations such as hospitalized and pregnant patients and glucocorticoid users. Patients with SLE and their caregivers experience a substantially reduced HRQoL. SUMMARY: SLE is a costly disease that disproportionately affects disadvantaged populations. Future economic studies should measure not only direct costs, but also incorporate indirect costs and the HRQoL of both patients with SLE and their caregivers. All these components are essential to provide a comprehensive assessment of the socioeconomic consequences of SLE and an appreciation of the potential impact of novel therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".