Short-term Risk of Total Malignancy and Nonmelanoma Skin Cancers with Certolizumab and Golimumab in Patients with Rheumatoid Arthritis: Metaanalysis of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of total malignancy and nonmelanoma skin cancers (NMSC) in patients with rheumatoid arthritis (RA) receiving certolizumab and golimumab through a metaanalysis of data from randomized control trials (RCT). METHODS: We systematically reviewed the literature up to May 2011 in Medline databases, as well as abstracts from the 2009 and 2010 annual meetings of the European League Against Rheumatism and the American College of Rheumatology. Mantel-Haenszel method was used to determine a common odds ratio (OR). Statistical heterogeneity was assessed by chi-square Q test. We selected only RCT including more than 30 RA subjects randomly assigned to an anti-tumor necrosis factor (TNF) or a nonbiological disease-modifying antirheumatic drug (DMARD) control group. RESULTS: The literature search identified 793 articles; 6 (2 with certolizumab and 4 with golimumab) were selected for metaanalysis. A total of 2710 patients received at least 1 dose of certolizumab or golimumab. For anti-TNF-treated patients, 18 cancers (excluding NMSC) and 9 NMSC were observed versus 4 cases of total malignancy and 3 NMSC in control groups. Metaanalysis revealed a pooled OR of 1.06 (95% CI 0.39-2.85) for risk of total malignancy and 0.69 (95% CI 0.23-2.11) for risk of NMSC with certolizumab and golimumab versus DMARD. Heterogeneity was not significant. CONCLUSION: Metaanalysis of RCT of golimumab and certolizumab did not find an increased risk of total malignancy and NMSC. These results must be confirmed with longterm extension studies and registry studies, and careful monitoring remains mandatory.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.064 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".