Medical History, Lifestyle, Family History, and Occupational Risk Factors for Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma: The InterLymph Non-Hodgkin Lymphoma Subtypes Project
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Chronic lymphocytic leukemia (CLL) and small lymphocytic lymphoma (SLL) are two subtypes of non-Hodgkin lymphoma. A number of studies have evaluated associations between risk factors and CLL/SLL risk. However, these associations remain inconsistent or lacked confirmation. This may be due, in part, to the inadequate sample size of CLL/SLL cases. METHODS: We performed a pooled analysis of 2440 CLL/SLL cases and 15186 controls from 13 case-control studies from Europe, North America, and Australia. We evaluated associations of medical history, family history, lifestyle, and occupational risk factors with CLL/SLL risk. Multivariate logistic regression analyses were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: We confirmed prior inverse associations with any atopic condition and recreational sun exposure. We also confirmed prior elevated associations with usual adult height, hepatitis C virus seropositivity, living or working on a farm, and family history of any hematological malignancy. Novel associations were identified with hairdresser occupation (OR = 1.77, 95% CI = 1.05 to 2.98) and blood transfusion history (OR = 0.79, 95% CI = 0.66 to 0.94). We also found smoking to have modest protective effect (OR = 0.9, 95% CI = 0.81 to 0.99). All exposures showed evidence of independent effects. CONCLUSIONS: We have identified or confirmed several independent risk factors for CLL/SLL supporting a role for genetics (through family history), immune function (through allergy and sun), infection (through hepatitis C virus), and height, and other pathways of immune response. Given that CLL/SLL has more than 30 susceptibility loci identified to date, studies evaluating the interaction among genetic and nongenetic factors are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it