Polyautoimmunity in Patients With Anticyclic Citrullinated Peptide Antibody–Positive and –Negative Rheumatoid Arthritis: a Nationwide Cohort Study From Denmark
Bibliographic record
Abstract
OBJECTIVE: This study aimed to compare the prevalence and incidence of polyautoimmunity between anticyclic citrullinated peptide antibody (anti-CCP)-positive and anti-CCP-negative patients with rheumatoid arthritis (RA). METHODS: In a nationwide register-based cohort study, patients with RA (disease duration ≤ 2 yrs) in the DANBIO rheumatology register with an available anti-CCP test in the Register of Laboratory Results for Research were identified. The polyautoimmunity outcome included 21 nonrheumatic autoimmune diseases identified by linkage between the Danish Patient Registry and Prescription Registry. The age- and sex-adjusted prevalence ratio (PR) was calculated by modified Poisson regression to estimate the prevalence at diagnosis in anti-CCP-positive vs anti-CCP-negative patients. The hazard ratio (HR) of polyautoimmunity within 5 years of entry into DANBIO was estimated in cause-specific Cox regression models. RESULTS: The study included 5839 anti-CCP-positive and 3799 anti-CCP-negative patients with RA. At first visit, the prevalence of prespecified polyautoimmune diseases in the Danish registers was 11.1% and 11.9% in anti-CCP-positive and anti-CCP-negative patients, respectively (PR 0.93, 95% CI 0.84-1.05). The most frequent autoimmune diseases were autoimmune thyroid disease, inflammatory bowel disease, and type 1 diabetes mellitus. During a mean follow-up of 3.5 years, only a few (n = 210) patients developed polyautoimmunity (HR 0.6, 95% CI 0.46-0.79). CONCLUSION: Polyautoimmunity as captured through the Danish National Patient Registry occurred in approximately 1 in 10 patients with RA at time of diagnosis regardless of anti-CCP status. In the years subsequent to the RA diagnosis, only a few and mainly anti-CCP-negative patients developed autoimmune disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".