Benefits of Autoantibody Enrichment in Early Rheumatoid Arthritis: Analysis of Efficacy Outcomes in Four Pooled Abatacept Trials
Bibliographic record
Abstract
INTRODUCTION: The efficacy of abatacept is enhanced in anti-citrullinated protein antibody (ACPA) and rheumatoid factor (RF)-positive versus -negative patients with rheumatoid arthritis (RA). Four early RA abatacept trials were analyzed to understand the differential impact of abatacept among patients with SeroPositive Early and Active RA (SPEAR) compared to non-SPEAR patients. METHODS: Pooled patient-level data from AGREE, AMPLE, AVERT, and AVERT-2 were analyzed. Patients were classified as SPEAR if they were ACPA +, RF +, disease duration < 1 year, and Disease Activity Score-28 (DAS28) C-reactive protein (CRP) ≥ 3.2 at baseline; non-SPEAR otherwise. Outcomes included: American College of Rheumatology (ACR) 20/50/70 at week 24; mean change from baseline to week 24 for DAS28 (CRP), Simple Disease Activity Index (SDAI), ACR core components; DAS28 (CRP) and SDAI remission. Adjusted regression analyses among abatacept-treated patients compared SPEAR and non-SPEAR patients, and in full trial population estimating how the efficacy of abatacept versus comparators [adalimumab + methotrexate, methotrexate] was modified by SPEAR status. RESULTS: The study included 1400 SPEAR and 673 non-SPEAR patients; most were female (79.35%), white (77.38%), and with a mean age 49.26 (SD 12.86) years old. Around half with non-SPEAR were RF + and three-quarters ACPA +. Stronger improvements from baseline to week 24 were observed in almost all outcomes for abatacept-treated SPEAR versus non-SPEAR patients or versus SPEAR patients treated with comparators. Larger improvements were observed for SPEAR patients among the abatacept-treated population, and more strongly improved efficacy among SPEAR patients for abatacept than comparators. CONCLUSIONS: This analysis, including large patient numbers of early-RA abatacept trials, confirmed beneficial treatment effects of abatacept in patients with SPEAR versus non-SPEAR.
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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 | 0.038 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".