Effect of Anticitrullinated Protein Antibody Status on Response to Abatacept or Antitumor Necrosis Factor-α Therapy in Patients with Rheumatoid Arthritis: A US National Observational Study
Bibliographic record
Abstract
Objective. Assess whether baseline anticyclic citrullinated peptide antibodies (anti-CCP) status is associated with treatment response in patients with rheumatoid arthritis (RA) initiating abatacept (ABA) or a tumor necrosis factor-α inhibitor (TNFi). Methods. Using the Corrona RA registry, patients were identified who initiated ABA or a TNFi (June 2004–January 2015), had a followup visit 6 months (± 3 mos) after initiation, and anti-CCP measured at or prior to initiation. Primary outcome was mean change in Clinical Disease Activity Index (CDAI) from initiation to 6 months. Treatment response was evaluated based on a typical patient profile (female, aged 57 yrs, body mass index of 30 kg/m2, baseline CDAI of 20, 1 prior biologic, and no comorbidities other than RA). Secondary outcomes included remission and low disease activity. Results. There were 566 ABA initiators [anti-CCP+ (≥ 20 units/ml): n = 362; anti-CCP− (< 20 units/ml): n = 204] and 1715 TNFi initiators (anti-CCP+: n = 1113; anti-CCP−: n = 602). Differences between treatment groups included baseline disease duration, CDAI, and prior biologic use. At 6 months, anti-CCP+ ABA initiators were associated with significantly greater CDAI response versus anti-CCP− ABA initiators; no significant difference was observed for TNFi initiators. When considering a typical RA patient profile, CDAI response was greater in anti-CCP+ versus anti-CCP− ABA initiators; anti-CCP+ versus anti-CCP− TNFi initiators were similar. Secondary outcome responses were also greater in anti-CCP+ versus anti-CCP− ABA initiators; TNFi initiators did not differ by anti-CCP status. Conclusion. In a US-based clinical practice setting, anti-CCP status was associated with a differential treatment response to ABA, but not TNFi.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".