First-degree Relatives of Patients with Rheumatoid Arthritis Exhibit High Prevalence of Joint Symptoms
Bibliographic record
Abstract
OBJECTIVE: The preclinical period of rheumatoid arthritis (RA) is characterized by the presence of autoantibodies such as anticitrullinated protein antibodies (ACPA) and rheumatoid factor (RF). Little is known about the joint symptom profile preceding onset of RA, and whether symptoms are associated with RA autoantibodies. Because first-degree relatives (FDR) of North American Native (NAN) RA probands exhibit multiple risk factors for development of future RA, we investigated the prevalence of joint symptoms in this high-risk population. METHODS: We studied 306 FDR of NAN patients with RA, 323 NAN controls (NC), and 293 white controls (WC) having no family history of autoimmune diseases. Study subjects completed a questionnaire that asked whether they had pain, swelling, or morning stiffness in their hand joints, or in other joints. Serum samples were gathered at the same time and tested for the presence of ACPA, RF, and high-sensitivity C-reactive protein levels. RESULTS: In all cases, FDR were significantly more likely to report experiencing joint symptoms compared to the 2 control groups. FDR also exhibited a significantly higher prevalence of RA autoantibodies than the control groups. There were modest trends for joint symptoms to associate with RA autoantibodies, and individuals who were both ACPA-positive and RF-positive had the highest prevalence of joint symptoms. CONCLUSION: FDR of NAN patients with RA have a higher prevalence of joint symptoms compared to individuals with no family history of autoimmune disease. This finding is only partially explained by a high prevalence of RA autoantibodies in the FDR.
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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 | 0.000 |
| 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) | 0.003 | 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".