Malignancy Risk in Systemic Lupus: Recent Research and Ongoing Challenges
Bibliographic record
Abstract
What is the magnitude of cancer risk in systemic lupus erythematosus (SLE) compared to the general population? Recent data confirmed a slight increased risk in SLE for all cancers combined, as well as a moderate increased risk of lung cancer, and a strikingly increased risk for hematological malignancies. The hematological cancer type most clearly elevated in SLE is non-Hodgkins lymphoma (NHL); Hodgkins lymphoma appears to be increased as well. In SLE, the most commonly identified NHL subtype is diffuse large B-cell lymphoma. Recent analyses suggest that lymphoma in autoimmune rheumatic diseases, including SLE, often presents extra-nodally and/or in advanced stages. Some data suggest that mortality risk in SLE patients with NHL has a bimodal pattern, with a number of patients succumbing early on, and the remainder experiencing fairly good survival rates. Key issues remaining under study relate to the links between cancer risk, clinical features, and medication exposures. New data suggest that disease-related factors may be as or more important, compared to other exposures such as immunosuppressive therapy. The challenge of establishing the independent influences of medication exposures versus disease activity on the risk of malignancy in SLE remains. Work in progress should shed light on these very important issues. Keywords: Malignancy, cancer, systemic lupus erythematosus, SLE, lymphoma, NHL
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".