The Impact of Rheumatoid Arthritis on First Nations and How We Can Work With Communities to Prevent It
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is prevalent in many Indigenous North American First Nations (FN) and tends to be seropositive, familial, and disabling, as well as associated with highly unfavorable outcomes such as early mortality. The risk of developing RA is based on a perfect storm of gene-environment interactions underpinning this risk. The gene-environment interactions include a high frequency of shared epitope encoding HLA alleles, particularlyHLA-DRB1*1402, in the background population, and prevalent predisposing environmental factors such as smoking and periodontal disease. Together, these provide a compelling rationale for an RA prevention agenda in FN communities. Our research team has worked in partnership with several FN communities to prospectively follow the first-degree relatives of FN patients with RA, with the aim of better understanding the preclinical stages of RA in this population. We have focused on specific features of the anticitrullinated protein antibodies (ACPA) and other proteomic biomarkers as predictors of future development of RA. These studies have now led us to consider interventions having a favorable risk-benefit ratio if applied at a stage prior to a hypothetical "point of no return," when the autoimmunity potentially becomes irreversible. Based on a supportive mouse model and available human studies of curcumin, omega-3, and vitamin D supplements, we are undertaking studies where we screen communities using dried blood spot technology adapted for the detection of ACPA, and then enrolling ACPA-positive individuals in studies that use a combination of these supplements. These studies are guided by shared decision-making principles.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".