Medical History, Lifestyle, Family History, and Occupational Risk Factors for Diffuse Large B-Cell Lymphoma: The InterLymph Non-Hodgkin Lymphoma Subtypes Project
Bibliographic record
Abstract
BACKGROUND: Although risk factors for diffuse large B-cell lymphoma (DLBCL) have been suggested, their independent effects, modification by sex, and association with anatomical sites are largely unknown. METHODS: In a pooled analysis of 4667 cases and 22639 controls from 19 studies, we used stepwise logistic regression to identify the most parsimonious multivariate models for DLBCL overall, by sex, and for selected anatomical sites. RESULTS: DLBCL was associated with B-cell activating autoimmune diseases (odds ratio [OR] = 2.36, 95% confidence interval [CI] = 1.80 to 3.09), hepatitis C virus seropositivity (OR = 2.02, 95% CI = 1.47 to 2.76), family history of non-Hodgkin lymphoma (OR = 1.95, 95% CI = 1.54 to 2.47), higher young adult body mass index (OR = 1.58, 95% CI = 1.12 to 2.23, for 35+ vs 18.5 to 22.4 kg/m(2)), higher recreational sun exposure (OR = 0.78, 95% CI = 0.69 to 0.89), any atopic disorder (OR = 0.82, 95% CI = 0.76 to 0.89), and higher socioeconomic status (OR = 0.86, 95% CI = 0.79 to 0.94). Additional risk factors for women were occupation as field crop/vegetable farm worker (OR = 1.78, 95% CI = 1.22 to 2.60), hairdresser (OR = 1.65, 95% CI = 1.12 to 2.41), and seamstress/embroider (OR = 1.49, 95% CI = 1.13 to 1.97), low adult body mass index (OR = 0.46, 95% CI = 0.29 to 0.74, for <18.5 vs 18.5 to 22.4 kg/m(2)), hormone replacement therapy started age at least 50 years (OR = 0.68, 95% CI = 0.52 to 0.88), and oral contraceptive use before 1970 (OR = 0.78, 95% CI = 0.62 to 1.00); and for men were occupation as material handling equipment operator (OR = 1.58, 95% CI = 1.02 to 2.44), lifetime alcohol consumption (OR = 0.57, 95% CI = 0.44 to 0.75, for >400 kg vs nondrinker), and previous blood transfusion (OR = 0.69, 95% CI = 0.57 to 0.83). Autoimmune disease, atopy, and family history of non-Hodgkin lymphoma showed similar associations across selected anatomical sites, whereas smoking was associated with central nervous system, testicular and cutaneous DLBCLs; inflammatory bowel disease was associated with gastrointestinal DLBCL; and farming and hair dye use were associated with mediastinal DLBCL. CONCLUSION: Our results support a complex and multifactorial etiology for DLBCL with some variation in risk observed by sex and anatomical site.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".