Does Anti-Tumor Necrosis Factor-α Therapy Affect Risk of Serious Infection and Cancer in Patients with Rheumatoid Arthritis?: A Review of Longterm Data
Bibliographic record
Abstract
Given the important role tumor necrosis factor-α (TNF-α) antagonists play in managing rheumatoid arthritis and the concern for safety during longterm therapy, we reviewed the latest evidence regarding longterm risk of infection and malignancy with TNF-α antagonists. Our objective was to provide clinicians with information that can be used to counsel and monitor patients who may be candidates for biologic therapy for rheumatoid arthritis (RA). Risk is examined in the context of background infection and malignancy rates in RA. Randomized controlled trial (RCT) data and observational studies summarizing the risk of infection and/or malignancy in RA and specific risks associated with the use of anti-TNF-α biologic agents (adalimumab, infliximab, and etanercept) were identified through a PubMed search. Overall, patients with RA appear to have an approximately 2-fold increased risk of serious infection compared to the general population and non-RA controls, irrespective of TNF-α antagonist use. Although data on infection rates with TNF-α antagonist use are contradictory, caution is merited. Recent analyses suggest that the risk of infection is highest within the first year. Regarding malignancy risk, RCT and observational data are also conflicting; how ever, caution is warranted regarding lymphoproliferative cancers in children and adolescents.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".