Severe Extraarticular Manifestations in a Community-based Cohort of Patients with Rheumatoid Arthritis: Risk Factors and Incidence in Relation to Treatment with Tumor Necrosis Factor Inhibitors
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to evaluate whether treatment with tumor necrosis factor (TNF) inhibitors in patients with rheumatoid arthritis (RA) affects the risk of developing severe extraarticular rheumatoid arthritis (ExRA) manifestations and to investigate potential predictors for developing ExRA. METHODS: A dynamic community-based cohort of patients with RA was studied (n = 1977). Clinical records were reviewed and cases of severe ExRA were identified. Information on exposure to TNF inhibitors was obtained from a regional register. Exposure to TNF inhibitors was analyzed in a time-dependent fashion and the incidence of severe ExRA in exposed patients was compared with the incidence in unexposed patients. Cox regression models were used to assess potential predictors of severe ExRA. RESULTS: During treatment with TNF inhibitors, there were 17 patients with new onset of severe ExRA in 2400 person-years at risk (PY; 0.71/100 PY, 95% CI 0.41-1.13) compared with 104 in 15,599 PY (0.67/100 PY, 95% CI 0.54-0.81) in patients without TNF inhibitors. This corresponded to an incidence rate ratio of 1.06 (95% CI 0.60-1.78). The age- and sex-adjusted HR for ExRA in anti-TNF-treated patients was 1.21 (95% CI 1.02-1.43), with similar findings in models adjusted for time-dependent Health Assessment Questionnaire and propensity for anti-TNF treatment. Male sex, positive rheumatoid factor (RF), long disease duration, and greater disability were predictors for ExRA. CONCLUSION: This study suggests that patients treated with TNF inhibitors are at a slightly increased risk of developing severe ExRA. RF-positive patients with disabling disease of long duration were more likely to develop severe ExRA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".