Abatacept: A Novel Treatment for Moderate‐to‐Severe Rheumatoid Arthritis
Bibliographic record
Abstract
Rheumatoid arthritis is a chronic autoimmune disease that often leads to functional disability and reduced quality of life. The pathogenesis of synovial inflammation that is associated with this disease is thought to result from T-cell activation. To become fully activated, T cells require an antigen-specific signal through the T-cell receptor and a second signal through a costimulatory receptor. Abatacept is the first drug in a new class of disease-modifying antirheumatic drugs (DMARDs) known as selective costimulation modulators. Costimulation modulators block the second signal and decrease T-cell activation. Abatacept has been approved by the United States Food and Drug Administration for reducing signs and symptoms, inducing major clinical response, slowing the progression of structural damage, and improving physical function in adults with moderate-to-severe active rheumatoid arthritis who have had an inadequate response to at least one other DMARD, such as methotrexate or tumor necrosis factor (TNF)-alpha inhibitors. Randomized controlled trials have shown that abatacept improves both clinical outcomes and health-related quality of life in patients who have had an inadequate response to other DMARDs. Abatacept has been shown to be well tolerated. In clinical trials, however, abatacept treatment was associated with a higher rate of infections compared with placebo. This finding was compounded when abatacept was used with TNF-alpha inhibitors; thus, this combination should be avoided. Abatacept appears to be a useful treatment option for patients with rheumatoid arthritis who have previously failed other DMARDs. However, additional clinical trials evaluating its long-term effect on patient safety and disease outcomes are needed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".