Disparities in rheumatoid arthritis outcomes for North American Indigenous populations
Bibliographic record
Abstract
Advances in rheumatoid arthritis (RA) management have significantly improved clinical outcomes of this disease; however, some Indigenous North Americans (INA) with RA have not achieved the high rates of treatment success observed in other populations. We review factors contributing to poor long-term outcomes for INA with RA. We conducted a narrative review of studies evaluating RA in INA supplemented with regional administrative health and clinical cohort data on clinical outcomes and health care utilisation. We discuss factors related to conducting research in INA populations including studies of RA prevention. NA with RA have a high burden of genetic and environmental predisposing risk factors that may impact disease phenotype, delayed or limited access to rheumatology care and advanced therapy. These factors may contribute to the observed increased rates of persistent synovitis, premature end-stage joint damage and mortality. Novel models of care delivery that are culturally sensitive and address challenges associated with providing speciality care to patients residing in remote communities with limited accessibility are needed. Progress in establishing respectful research partnerships with INA communities has created a foundation for ongoing initiatives to address care gaps including those aimed at RA prevention. This review highlights some of the challenges of diagnosing, treating, and ultimately perhaps preventing, RA in INA populations.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".