Global Rural and Remote Patients With Rheumatoid Arthritis: A Systematic Review
Bibliographic record
Abstract
Objective Rural and remote patients with rheumatoid arthritis (RA) are at risk for inequities in health outcomes based on differences in physical environments and health care access potential compared to urban populations. The aim of this systematic review was to synthesize epidemiology, clinical outcomes, and health service use reported for global populations with RA residing in rural and remote locations. Methods Medline, Embase, HealthStar, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and the Cochrane Library were searched from inception to June 2019 using librarian‐developed search terms for RA and rural and remote populations. Peer‐reviewed published manuscripts were included if they reported on epidemiologic, clinical, or health service use outcomes. Results Fifty‐four articles were included for data synthesis, representing studies from all continents. In 11 studies in which there was an appropriate urban population comparator, rural and remote populations were not at increased risk for RA; 1 study reported increased prevalence, and 5 studies reported decreased prevalence in rural and remote populations. Clinical characteristics of rural and remote populations in studies with an appropriate urban comparator showed no significant differences in disease activity measures or disability, but 1 study reported worse physical function and health‐related quality of life in rural and remote populations. Studies reporting on health service use provided evidence that rural and remote residence adversely impacts diagnostic time, ongoing follow‐up, access to RA‐care–related practitioners and services, and variation in medication access and use, with prominent heterogeneity noted between countries. Conclusion RA epidemiology and clinical outcomes are not necessarily different between rural/remote and urban populations within countries. Rural and remote patients face greater barriers to care, which increases the risk for inequities in outcomes.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".