Safety of the selective costimulation modulator abatacept in rheumatoid arthritis patients receiving background biologic and nonbiologic disease‐modifying antirheumatic drugs: A one‐year randomized, placebo‐controlled study
Bibliographic record
Abstract
OBJECTIVE: To assess the safety of abatacept, a selective costimulation modulator, in patients with active rheumatoid arthritis (RA) who had been receiving > or =1 traditional nonbiologic and/or biologic disease-modifying antirheumatic drugs (DMARDs) approved for the treatment of RA for at least 3 months prior to entry into the study. METHODS: This was a 1-year, multicenter, randomized, double-blind, placebo-controlled trial. Patients were randomized 2:1 to receive abatacept at a fixed dose approximating 10 mg/kg by weight range, or placebo. RESULTS: The abatacept and placebo groups exhibited similar frequencies of adverse events (90% and 87%, respectively), serious adverse events (13% and 12%, respectively), and discontinuations due to adverse events (5% and 4%, respectively). Five patients (0.5%) in the abatacept group and 4 patients (0.8%) in the placebo group died during the study. Serious infections were more frequent in the abatacept group than in the placebo group (2.9% versus 1.9%). Fewer than 4% of patients in either group experienced a severe or very severe infection. The incidence of neoplasms was 3.5% in both groups. When evaluated according to background therapy, serious adverse events occurred more frequently in the subgroup receiving abatacept plus a biologic agent (22.3%) than in the other subgroups (11.7-12.5%). CONCLUSION: Abatacept in combination with synthetic DMARDs was well tolerated and improved physical function and physician- and patient-reported disease outcomes. However, abatacept in combination with biologic background therapies was associated with an increase in the rate of serious adverse events. Therefore, abatacept is not recommended for use in combination with biologic therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".