Family history of hematopoietic malignancies and risk of non-Hodgkin lymphoma (NHL): a pooled analysis of 10 211 cases and 11 905 controls from the International Lymphoma Epidemiology Consortium (InterLymph)
Bibliographic record
Abstract
A role for genetic susceptibility in non-Hodgkin lymphoma (NHL) is supported by the accumulating evidence of common genetic variations altering NHL risk. However, the pattern of NHL heritability remains poorly understood. We conducted a pooled analysis of 10 211 NHL cases and 11 905 controls from the International Lymphoma Epidemiology Consortium (InterLymph) to evaluate NHL risk among those with hematopoietic malignancies in first-degree relatives. Odds ratios (ORs) and 95% confidence intervals (CIs) of NHL and its subtypes were estimated from unconditional logistic regression models with adjustment for confounders. NHL risk was elevated for individuals who reported first-degree relatives with NHL (OR = 1.5; 95% CI = 1.2-1.9), Hodgkin lymphoma (OR = 1.6; 95% CI = 1.1-2.3), and leukemia (OR = 1.4; 95% CI = 1.2-2.7). Risk was highest among individuals who reported a brother with NHL (OR = 2.8; 95% CI = 1.6-4.8) and was consistent for all NHL subtypes evaluated. If a first-degree relative had Hodgkin lymphoma, NHL risk was highest if the relative was a parent (OR = 1.7; 95% CI = 1.0-2.9). If a first-degree relative had leukemia, NHL risk was highest among women who reported a sister with leukemia (OR = 3.0; 95% CI = 1.6-5.6). The pattern of NHL heritability appeared to be uniform across NHL subtypes, but risk patterns differed by specific hematopoietic malignancies and the sex of the relative, revealing critical clues to disease etiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".