Bibliographic record
Abstract
PURPOSE OF REVIEW: The association of cancer with autoimmune disease has been under investigation for several years. Reports have appeared suggesting increased cancer risk in autoimmune rheumatic diseases. Evidence has been accumulating recently in rheumatoid arthritis, Sjogren's syndrome, systemic lupus erythematosus, and scleroderma/systemic sclerosis. This review focuses on recent publications regarding risk of cancer in these conditions. RECENT FINDINGS: Despite a lack of a strong association between rheumatoid arthritis and cancer overall, studies show an increased risk for the development of lymphoma in rheumatoid arthritis. There are data suggesting an increased risk for rheumatoid arthritis patients regarding lung cancer. In Sjogren's syndrome-related malignancies, most publications in the past year relate to non-Hodgkin's lymphomas, and suggest possible mechanisms driving the association. Data substantiate an increased risk of certain cancers in systemic lupus erythematosus; the risk appears to be most heightened for lymphoma. A recent cohort study examined cancer risk in scleroderma; the estimates were lower than previous studies had suggested, and the confidence intervals relatively imprecise, making a definitive conclusion difficult. SUMMARY: There have been several papers published related to cancer in the rheumatic diseases, particularly inflammatory arthritis, Sjogren's syndrome, systemic lupus erythematosus, and scleroderma/systemic sclerosis. Continuing interest in the association between autoimmune rheumatic diseases and malignancy is likely, given the potential impact in terms of understanding both rheumatic diseases and cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".