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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".