Autoimmune disorders and risk of non-Hodgkin lymphoma subtypes: a pooled analysis within the InterLymph Consortium
Bibliographic record
Abstract
Some autoimmune disorders are increasingly recognized as risk factors for non-Hodgkin lymphoma (NHL) overall, but large-scale systematic assessments of risk of NHL subtypes are lacking. We performed a pooled analysis of self-reported autoimmune conditions and risk of NHL and subtypes, including 29 423 participants in 12 case-control studies. We computed pooled odds ratios (OR) and 95% confidence intervals (CI) in a joint fixed-effects model. Sjögren syndrome was associated with a 6.5-fold increased risk of NHL, a 1000-fold increased risk of parotid gland marginal zone lymphoma (OR = 996; 95% CI, 216-4596), and with diffuse large B-cell and follicular lymphomas. Systemic lupus erythematosus was associated with a 2.7-fold increased risk of NHL and with diffuse large B-cell and marginal zone lymphomas. Hemolytic anemia was associated with diffuse large B-cell NHL. T-cell NHL risk was increased for patients with celiac disease and psoriasis. Results for rheumatoid arthritis were heterogeneous between studies. Inflammatory bowel disorders, type 1 diabetes, sarcoidosis, pernicious anemia, and multiple sclerosis were not associated with risk of NHL or subtypes. Thus, specific autoimmune disorders are associated with NHL risk beyond the development of rare NHL subtypes in affected organs. The pattern of associations with NHL subtypes may harbor clues to lymphomagenesis.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".