Non-Hodgkin Lymphoma Risk and Variants in Genes Controlling Lymphocyte Development
Bibliographic record
Abstract
Non-Hodgkin lymphomas (NHL) are a heterogeneous group of solid tumours of lymphoid cell origin. Three important aspects of lymphocyte development include immunity and inflammation, DNA repair, and programmed cell death. We have used a previously established case-control study of NHL to ask whether genetic variation in genes involved in these three important processes influences risk of this cancer. 118 genes in these three categories were tagged with single nucleotide polymorphisms (SNPs), which were tested for association with NHL and its subtypes. The main analysis used logistic regression (additive model) to estimate odds ratios in European-ancestry cases and controls. 599 SNPs and 1116 samples (569 cases and 547 controls) passed quality control measures and were included in analyses. Following multiple-testing correction, one SNP in MSH3, a mismatch repair gene, showed an association with diffuse large B-cell lymphoma (OR: 1.91; 95% CI: 1.41-2.59; uncorrected p = 0.00003; corrected p = 0.010). This association was not replicated in an independent European-ancestry sample set of 251 diffuse large B-cell lymphoma cases and 737 controls, indicating this result was likely a false positive. It is likely that moderate sample size, inter-subtype and other genetic heterogeneity, and small true effect sizes account for the lack of replicable findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".