Impact of rural residence on clinical outcomes in SLE: a systematic review
Bibliographic record
Abstract
BACKGROUND: SLE is a chronic autoimmune disease with heterogeneous manifestations and variable outcomes. Geographic factors such as rural residence may influence disease severity, access to care and treatment adherence, yet evidence remains fragmented. This systematic review aimed to evaluate the impact of rurality on clinical outcomes in adults with SLE. METHODS: We systematically searched PubMed, Embase and Web of Science for observational studies published up to May 2025 that assessed the association between rural residence and clinical outcomes in SLE. Eligible studies included adult patients with SLE and reported at least one relevant outcome stratified by rurality. Using the Newcastle-Ottawa Scale, data were extracted on study characteristics, definitions of rurality, outcome domains and risk of bias. Due to heterogeneity in study design and outcomes, a narrative synthesis was conducted. RESULTS: Eight studies, including over 34 000 participants from the USA, Greece, China, Egypt, Puerto Rico and Latin America (Grupo Latino Americano de Estudio del Lupus cohort), met inclusion criteria. Definitions of rurality varied widely, encompassing administrative classifications, demographic thresholds and self-reported residence. Rural residence was often associated with delayed diagnosis, higher disease activity, lower physical quality of life, increased hospital readmissions and poorer medication adherence. Survival findings were mixed, and one study found no rural disadvantage where specialised care was available. Methodological quality was generally moderate to high. CONCLUSION: Across diverse settings, rural SLE populations frequently experience worse outcomes, although this is not universal and appears to be strongly influenced by structural disadvantages rather than geography alone. Standardised definitions of rurality and multidimensional measurement approaches are needed to improve comparability and guide effective interventions. Targeted strategies-such as telemedicine, outreach programmes and policies addressing healthcare access-may help reduce inequities in SLE care.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".