Medikamentöse Therapie der rheumatoiden Arthritis bei Malignomanamnese
Bibliographic record
Abstract
INTRODUCTION: Only insufficient data are available regarding the question whether treatment with immunosuppressants or biologicals is feasible and safe in patients with a history of malignancy. METHOD: Literature search via PubMed, EULAR abstracts and ACR abstracts from 2013 to 2015. RESULTS: The Société Francaise de Rhumatologie, the Canadian Rheumatology Association and the American College of Rheumatology have tried to make recommendations on this topic. Direct evidence mainly originates from data in three national registries which suggest that treatment with tumor necrosis factor (TNF) inhibitors and rituximab appears to be safe for carefully selected patients, at least if there is a longer interval between treatment with biologicals and oncological treatment. Furthermore, despite partly conflicting data all routine drugs for treating rheumatoid arthritis do not seem to show a consistently increased risk of de novo malignancies. The currently available data are presented for each drug of interest. CONCLUSION: Taking the current literature into account an attempt is made to formulate an algorithm for the medicinal treatment of patients with rheumatoid arthritis and a history of malignancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.068 |
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; both teacher heads agree on what is shown here.
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".