Burden of Disease and Drug Response for Patients With Rheumatoid Arthritis by Shared Epitope and Anticitrullinated Protein Antibody Status
Bibliographic record
Abstract
Objective To characterize disease burden among adults with rheumatoid arthritis (RA) by both shared epitope (SE) and anticitrullinated protein antibody (ACPA) status, and to determine how their responses to abatacept, tumor necrosis inhibitors, and Janus kinase inhibitors may differ. Methods Using data from 2 observational cohorts (FORWARD, Veterans Affairs RA [VARA]), individuals with RA were classified by SE/ACPA status. Outcomes included disease activity (Patient Activity Scale II [PAS-II], Routine Assessment of Patient Index Data 3 [RAPID3], Disease Activity Score in 28 joints), Rheumatic Disease Comorbidity Index (RDCI), lifetime disease-modifying antirheumatic drug (DMARD) exposure, and healthcare usage. Differences by SE/ACPA classification were determined with multiple linear regression. Response to DMARD initiation was assessed with linear regression for continuous measures of disease activity and logistic regression for achieving a change as large as the minimum clinically important difference. Results A total of 3243 individuals were included (FORWARD, n = 917; VARA n = 2326). RDCI among ACPA-negative individuals was lower than in ACPA-positive individuals in both cohorts (β [95% CI]: FORWARD −0.35 [−0.65 to −0.05], P = 0.02; VARA −0.35 [−0.63 to −0.08], P = 0.01). In FORWARD, there were significant differences in disease burden, including lower disease activity (PAS-II −0.77 [−1.10 to −0.44], P < 0.001), lower healthcare usage (rheumatology visits −0.18 [−0.35 to 0.0], P = 0.046), and higher DMARD counts (0.43 [0.02 to 0.85], P = 0.04) among SE+/ACPA+ individuals. ACPA+ abatacept initiators were more likely to experience clinically important improvements in PAS-II and DAS28, but RAPID3 was not significantly associated with abatacept response. Conclusion Our results highlight important differences in disease burden by SE/ACPA status and suggest that ACPA status, rather than correlative SE status, may be the stronger predictor of abatacept response among individuals with RA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".