Genetic Variations in SMAD7 Are Associated with Colorectal Cancer Risk in the Colon Cancer Family Registry
Bibliographic record
Abstract
BACKGROUND: Recent genome-wide studies identified a risk locus for colorectal cancer at 18q21, which maps to the SMAD7 gene. Our objective was to confirm the association between SMAD7 SNPs and colorectal cancer risk in the multi-center Colon Cancer Family Registry. MATERIALS AND METHODS: 23 tagging SNPs in the SMAD7 gene were genotyped among 1,592 population-based and 253 clinic-based families. The SNP-colorectal cancer associations were assessed in multivariable conditional logistic regression. RESULTS: Among the population-based families, both SNPs rs12953717 (odds ratio, 1.29; 95% confidence interval, 1.12-1.49), and rs11874392 (odds ratio, 0.80; 95% confidence interval, 0.70-0.92) were associated with risk of colorectal cancer. These associations were similar among the population- and the clinic-based families, though they were significant only among the former. Marginally significant differences in the SNP-colorectal cancer associations were observed by use of nonsteroidal anti-inflammatory drugs, cigarette smoking, body mass index, and history of polyps. CONCLUSIONS: SMAD7 SNPs were associated with colorectal cancer risk in the Colon Cancer Family Registry. There was evidence suggesting that the association between rs12953717 and colorectal cancer risk may be modified by factors such as smoking and use of nonsteroidal anti-inflammatory drugs.
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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 | 0.007 |
| 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) | 0.003 | 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".