Interaction of methylenetetrahydrofolate reductase genotype and smoking habit in Taiwanese lung cancer patients.
Bibliographic record
Abstract
UNLABELLED: The aim of this study was to evaluate the association and interaction of genotypic polymorphisms in methylenetetrahydrofolate reductase (MTHFR) and environmental factors with lung cancer in Taiwan. Two well-known polymorphic variants of MTHFR, C677T (rs1801133) and A1298C (rs1801131), were analyzed in association with lung cancer susceptibility, and discussed their joint effects with individual habits on lung cancer risk. PATIENTS AND METHODS: In total, 358 patients with lung cancer and 716 healthy controls recruited from the China Medical Hospital in central Taiwan were genotyped. RESULTS: The MTHFR C677T genotype, but not the A1298C, was differently distributed between the lung cancer and control groups. The T allele of MTHFR C677T was significantly more frequently found in controls than in cancer patients. As for A1298C polymorphism, there was no difference in distribution between the lung cancer and control groups. Gene interactions with smoking were significant for MTHFR C677T polymorphism. The MTHFR C677T CT and TT genotypes in association with smoking conferred a decreased risk of 0.706 (95% confidence interval=0.531-0.939) for lung cancer. CONCLUSION: Our results provide the first evidence that the C allele of MTHFR C677T may be associated with the development of lung cancer and may be a novel useful marker for primary prevention and anticancer intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".