A Prospective Study of the Development of Inflammatory Arthritis in the Family Members of Indigenous North American People With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of inflammatory arthritis and autoantibody prevalence in Indigenous North American people. METHODS: Unaffected relatives of Indigenous North Americans with rheumatoid arthritis (RA) from central Canada and Alaska were systematically monitored from 2005 to 2017. Rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPAs) were tested at every visit, and a subset was tested for ACPA fine specificity using a custom multiplex assay. Multistate models based on all available study visits were developed to determine the likelihood of transitioning between autoantibody states, or to inflammatory arthritis. RESULTS: Eighteen of 374 relatives (4.8%) developed inflammatory arthritis during follow-up (after a mean ± SD of 4.7 ± 2.4 years), yielding a transition rate of 9.2 cases/1,000 person-years. Thirty percent of those who developed inflammatory arthritis were seronegative at baseline, but all were seropositive at inflammatory arthritis onset. Although 30% of ACPA/RF double-seropositive individuals developed inflammatory arthritis (after 3.2 ± 2.2 years), the majority of these individuals did not develop inflammatory arthritis. Multistate modeling indicated a 71% and 68% likelihood of ACPA and RF seropositive states, respectively, reverting to a seronegative state after 5 years, and a 39% likelihood of an ACPA/RF double-seropositive state becoming seronegative. Fine specificity testing demonstrated an expansion of the ACPA repertoire prior to the development of inflammatory arthritis. CONCLUSION: Despite a high incidence of inflammatory arthritis in this cohort of at-risk relatives of Indigenous North Americans with RA, a large proportion of autoantibody-positive individuals do not develop inflammatory arthritis and revert back to an autoantibody-negative state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".