Abatacept for Rheumatoid Arthritis: A Cochrane Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review of efficacy and safety of abatacept in patients with rheumatoid arthritis (RA). METHODS: We searched the Cochrane Library, MEDLINE, EMBASE, ACP Journal Club, and Biosis Previews for randomized controlled trials (RCT) comparing abatacept alone or in combination with disease modifying antirheumatic drugs (DMARD)/biologics to placebo or other DMARD/biologics in patients with RA. Two reviewers independently assessed search results, risk of bias, and extracted data. RESULTS: Seven trials with 2908 patients were included. Compared with placebo, patients with RA treated with abatacept were 2.2 times more likely to achieve an American College of Rheumatology 50% response (ACR50) at one year (relative risk 2.21, 95% CI 1.73, 2.82) with a 21% (95% CI 16%, 27%) absolute risk difference between groups. The number needed to treat to achieve an ACR50 response was 5 (95% CI 4, 7). Significantly greater improvements in physical function, disease activity, pain, and radiographic progression were noted in abatacept-treated patients compared to placebo. Total adverse events (AE) were greater in the abatacept group (RR 1.05, 95% CI 1.01, 1.08). Other harm outcomes were not significant, with the exception of serious infections at 12 months, which were more common in the abatacept group versus control group (Peto odds ratio 1.91, 95% CI 1.07, 3.42). Serious AE were more numerous in the abatacept + etanercept group versus the placebo + etanercept group (RR 2.30, 95% CI 1.15, 4.62). CONCLUSION: Abatacept seems to be efficacious and safe in the treatment of RA. Abatacept should not be used in combination with other biologics to treat RA. Further longterm studies and postmarketing surveillance are required to assess for longer-term harms and sustained efficacy.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".