Incidence and spectrum of typical complications associated with immunosuppressant use in rheumatic disease: A nationwide retrospective study
Bibliographic record
Abstract
AIM: To estimate the incidence and characteristics of typical complications associated with immunosuppressant therapy across various types of rheumatic disease (RD). METHODS: We conducted a retrospective analysis using Korean claims data, including patients who were first diagnosed with rheumatic disease (rheumatoid arthritis, spondyloarthrosis, lupus erythematosus, vasculitis, sclerosis, Sjögren's syndrome, and polymyalgia rheumatica) and initiated immunosuppressant therapy between 2015 and 2018. We focused on several typical complications, including infections, cancer, cardiovascular complications, bone marrow suppression, gastrointestinal complications, diabetes, interstitial lung disease, demyelinating disease, acute kidney injury, and hepatotoxicity. RESULTS: Among 464 753 patients prescribed immunosuppressants, 59 548 initiated treatment following their RD diagnosis. The most prevalent complications included opportunistic infections (60.6 events per 1000 person-years), serious infections (41.1), hypertension (31.5), diabetes (18.3), and cancer (14.1). Most complications peaked in the first month after treatment initiation. While the risk of serious infections and cancer increased with age, the incidence of opportunistic infections did not significantly vary. Furthermore, risk of serious infection or cancer did not significantly differ among RD types. CONCLUSIONS: This investigation elucidated the incidence and spectrum of typical complications associated with immunosuppressants among patients with RD, using a vast real-world dataset to highlight key safety concerns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".