Tumor necrosis factor therapy and the risk of serious infection and malignancy in patients with early rheumatoid arthritis: A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
OBJECTIVE: To conduct a meta-analysis of the rates of serious infection and malignancy in patients with early rheumatoid arthritis (RA) who have started anti-tumor necrosis factor (anti-TNF) therapy and had not received treatment with disease-modifying antirheumatic drugs (DMARDs) or methotrexate (MTX). METHODS: A systematic literature search was conducted through the summer of 2009. All studies included were randomized, double-blind, placebo-controlled trials involving patients with early RA who were started on anti-TNF therapy without prior DMARD/MTX use. Six trials met the inclusion criteria for the meta-analysis, comprising a total of 2,183 patients receiving biologic therapy and 1,236 patients receiving MTX. The data extracted were from published trials. RESULTS: A pooled odds ratio (OR) (determined using Mantel-Haenszel methods, with a continuity correction designed for sparse data) was calculated for serious infections (requiring hospitalization) and malignancies, comparing anti-TNF therapy to MTX control. The pooled OR for serious infections was 1.28 (95% confidence interval [95% CI] 0.82-2.00) and that for malignancies was 1.08 (95% CI 0.50-2.32). There was no significant difference in either the rate of serious infections or the rate of malignancies between the anti-TNF therapy group and the control group. CONCLUSION: Whereas other meta-analyses have shown an increased risk of serious infection and malignancy in patients receiving anti-TNF therapy, the results of the present meta-analysis show that there is not an increased risk when the patients have early disease and have not previously been treated with DMARDs and/or MTX.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.039 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".