Data from Assessing Tumor Mutations to Gain Insight into Base Excision Repair Sequence Polymorphisms and Smoking in Colon Cancer
Bibliographic record
Abstract
Abstract DNA repair enzymes function in major pathways to reverse DNA damage, including base excision repair (BER). Missense polymorphisms in BER repair genes may contribute to differences in DNA repair capacity, specific mutations, and susceptibility to cancer in the presence of exposure to carcinogens such as cigarette smoking. In a study of 1,604 incident colon cancer cases and 1,969 matched population-based controls genotyped for BER variants OGG1 (S326C) and XRCC1 (R194W, R280H, and R399Q), we found no associations with colon cancer overall. However, a 2-fold increased risk of BRAF V600E tumor mutation was observed in current and former cigarette smokers homozygous for the OGG1 polymorphism (odds ratio, 2.2; 95% confidence interval, 1.02-4.9, recessive model); similar associations were not observed for microsatellite instability, CpG island methylator phenotype, KRAS2 mutations, or TP53 mutations. The XRCC1 R194W polymorphism was associated with a modest increased risk of TP53 tumor mutations in those who regularly smoked cigarettes (odds ratio, 1.4; 95% confidence interval, 1.02-1.9). These findings point to the importance of studying tumor mutations when examining DNA repair polymorphisms and cigarette smoke exposure to identify potentially relevant associations with colorectal cancer. (Cancer Epidemiol Biomarkers Prev 2009;18(12):3384–8)
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".