Systematic Review of Recommendations on the Use of Disease‐Modifying Antirheumatic Drugs in Patients With Rheumatoid Arthritis and Cancer
Bibliographic record
Abstract
OBJECTIVE: To evaluate consensus recommendations regarding management of rheumatoid arthritis (RA) in patients with cancer. METHODS: We searched electronic databases, guideline registries, and relevant web sites for cancer-specific recommendations on RA management. Reviewers independently selected and appraised the recommendations according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument. We identified similarities and discrepancies among recommendations. RESULTS: Of 4,077 unique citations, 39 recommendations were identified, of which half described their consensus process. Average scores for the AGREE II domains ranged from 33% to 87%. Cancer risk in RA was addressed in 79% of recommendations, with acknowledgement of increased overall cancer risk. Recommendations did not agree on the safety of using disease-modifying antirheumatic drugs (DMARDs) in RA patients with cancer, except for the contraindication of tumor necrosis factor inhibitors in patients at risk for lymphoma. Most recommendations agreed that RA treatment should be stopped and re-evaluated with a new diagnosis of cancer. Recommendations for patients with a history of cancer differed depending on the drug, cancer type, and time since cancer diagnosis. Few recommendations addressed all issues. CONCLUSION: Recommendations for the treatment of RA in patients with cancer often fail to meet expected methodologic criteria. There was agreement on the need for caution when prescribing DMARDs to these patients. However, several areas continue to lack consensus, and given the paucity of evidence, there is an urgent need for research and expert opinion to guide and standardize the management of RA in patients with cancer.
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 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 | 0.037 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".