Medical History, Lifestyle, Family History, and Occupational Risk Factors for Marginal Zone Lymphoma: The InterLymph Non-Hodgkin Lymphoma Subtypes Project
Bibliographic record
Abstract
BACKGROUND: Marginal zone lymphoma (MZL), comprised of nodal, extranodal, and splenic subtypes, accounts for 5%-10% of non-Hodgkin lymphoma cases. A detailed evaluation of the independent effects of risk factors for MZL and its subtypes has not been conducted. METHODS: Data were pooled from 1052 MZL cases (extranodal [EMZL] = 633, nodal [NMZL] = 157, splenic [SMZL] = 140) and 13766 controls from 12 case-control studies. Adjusted unconditional logistic regression was used to compute odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Novel findings for MZL subtypes include increased risk for B-cell activating autoimmune conditions (EMZL OR = 6.40, 95% CI = 4.24 to 9.68; NMZL OR = 7.80, 95% CI = 3.32 to 18.33; SMZL OR = 4.25, 95% CI = 1.49 to 12.14), hepatitis C virus seropositivity (EMZL OR = 5.29, 95% CI = 2.48 to 11.28), self-reported peptic ulcers (EMZL OR = 1.83, 95% CI = 1.35 to 2.49), asthma without other atopy (SMZL OR = 2.28, 95% CI = 1.23 to 4.23), family history of hematologic cancer (EMZL OR = 1.90, 95% CI = 1.37 to 2.62) and of non-Hodgkin lymphoma (NMZL OR = 2.82, 95% CI = 1.33 to 5.98), permanent hairdye use (SMZL OR = 6.59, 95% CI = 1.54 to 28.17), and occupation as a metalworker (NMZL OR = 3.56, 95% CI = 1.67 to 7.58). Reduced risks were observed with consumption of any alcohol (EMZL fourth quartile OR = 0.48, 95% CI = 0.28 to 0.82) and lower consumption of wine (NMZL first to third quartile ORs < 0.45) compared with nondrinkers, and occupation as a teacher (EMZL OR = 0.58, 95% CI = 0.37 to 0.88). CONCLUSION: Our results provide new data suggesting etiologic heterogeneity across MZL subtypes although a common risk of MZL associated with B-cell activating autoimmune conditions was found.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".