Rheumatic Disease Among Oklahoma Tribal Populations: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVE: Rheumatic diseases cause significant morbidity within American Indian populations. Clinical disease presentations, as well as historically associated autoantibodies, are not always useful in making a rapid diagnosis or assessing prognosis. The purpose of our study was to identify autoantibody associations among Oklahoma tribal populations with rheumatic disease. METHODS: Oklahoma tribal members (110 patients with rheumatic disease and 110 controls) were enrolled at tribal-based clinics. Patients with rheumatic disease (suspected or confirmed diagnosis) were assessed by a rheumatologist for clinical features, disease criteria, and activity measures. Blood samples were collected and tested for common rheumatic disease autoantibodies [antinuclear antibody (ANA), anti-cyclic citrullinated peptide antibodies (anti-CCP), rheumatoid factor (RF), anti-Ro, anti-La, anti-Sm, anti-nRNP, anti-ribosomal P, anti-dsDNA, and anticardiolipins]. RESULTS: In patients with suspected systemic rheumatic diseases, 72% satisfied American College of Rheumatology classification criteria: 40 (36%) had rheumatoid arthritis (RA), 16 (15%) systemic lupus erythematosus, 8 (7%) scleroderma, 8 (7%) osteoarthritis, 4 (4%) fibromyalgia, 2 (2%) seronegative spondyloarthropathy, 1 Sjögren's syndrome, and 1 sarcoidosis. Compared to controls, RA patient sera were more likely to contain anti-CCP (55% vs 2%; p < 0.001) or RF IgM antibodies (57% vs 10%; p < 0.001); however, the difference was greater for anti-CCP. Anti-CCP positivity conferred higher disease activity scores (DAS28 5.6 vs 4.45; p = 0.021) while RF positivity did not (DAS28 5.36 vs 4.64; p = 0.15). Anticardiolipin antibodies (25% of rheumatic disease patients vs 10% of controls; p = 0.0022) and ANA (63% vs 21%; p < 0.0001) were more common in rheumatic disease patients. CONCLUSION: Anti-CCP may serve as a more specific RA biomarker in American Indian patients, while the clinical significance of increased frequency of anticardiolipin antibodies needs further evaluation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".