Does Abatacept Increase Postoperative Adverse Events in Rheumatoid Arthritis Compared with Conventional Synthetic Disease-modifying Drugs?
Bibliographic record
Abstract
OBJECTIVE: To investigate whether abatacept (ABA) causes more adverse events (AE) than conventional synthetic disease-modifying antirheumatic drugs (csDMARD) after orthopedic surgery in patients with rheumatoid arthritis (RA). METHODS: A retrospective multicenter nested case-control study was performed in 18 institutions. Patients receiving ABA (ABA group) were matched individually with patients receiving csDMARD and/or steroids (control group). Postoperative AE included surgical site infection, delayed wound healing, deep vein thrombosis or pulmonary embolism, flare, and death. The incidence rates of the AE in both groups were compared with the Mantel-Haenszel test. Risk factors for AE were analyzed by logistic regression model. RESULTS: A total of 3358 cases were collected. After inclusion and exclusion, 2651 patients were selected for matching, and 194 patients in 97 pairs were chosen for subsequent comparative analyses between the ABA and control groups. No between-group differences were detected in the incidence rates of each AE or in the incidence rates of total AE (control vs ABA: 15.5% vs 20.7% in total, 5.2% vs 3.1% in death). CONCLUSION: Compared with csDMARD and/or steroids without ABA, adding ABA to the treatment does not appear to increase the incidence rates of postoperative AE in patients with RA undergoing orthopedic surgery. Large cohort studies should be performed to add evidence for the perioperative safety profile of ABA.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".