Epidermal growth factor A61G gene polymorphism, gastroesophageal reflux disease and esophageal adenocarcinoma risk
Bibliographic record
Abstract
BACKGROUND: Single-nucleotide polymorphisms of key cancer genes, such as EGF A61G, are associated with an elevated risk of esophageal adenocarcinoma (EAC). As gastroesophageal reflux disease (GERD) is an established risk factor for EAC, we evaluated whether the association between epidermal growth factor (EGF) polymorphism and EAC development is altered by the presence of GERD. METHODS: EGF genotyping of DNA samples was performed and GERD history was collected for 309 EAC patients and 275 matched healthy controls. Associations between genotypes and EAC risk were evaluated using adjusted logistic regression. Genotype-GERD relationships were explored using analyses stratified by GERD history and joint effects models that considered severity and duration of GERD symptoms. RESULTS: EGF variants (A/G or G/G) were more common (P = 0.02) and GERD was more prevalent (P < 0.001) in cases than in controls. When compared with the EGF wild-type A/A genotype, the G/G variant was associated with a substantial increase in EAC risk among individuals with GERD [Odds ratio 9.7; 95% confidence interval (CI), 3.8-25.0; P < 0.001] and a slight decrease in risk for GERD-free individuals (odds ratio 0.4; 95% CI = 0.22-0.90; P = 0.02). In the joint effects models, the odds of EAC was also highest for G/G patients (when compared with A/A) who either experienced frequent GERD of more than once per week (odds ratio 21.8; 95% CI = 5.1-94.0; P < 0.001) or suffered GERD for longer than 15 years (odds ratio 22.4; 95% CI = 6.5-77.6; P < 0.001). There was a highly significant interaction between the G/G genotype and the presence of GERD (P < 0.001). CONCLUSIONS: EGF A61G polymorphism may alter EAC susceptibility through an interaction with GERD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".