Re-evaluation of ABO gene polymorphisms detected in a genome-wide association study and risk of pancreatic ductal adenocarcinoma in a Chinese population
Bibliographic record
Abstract
Pancreatic cancer is a fatal malignancy with an increasing incidence in Shanghai, China. A genome-wide association study (GWAS) and other work have shown that ABO alleles are associated with pancreatic cancer risk. We conducted a population-based case-control study involving 256 patients with pathologically confirmed pancreatic ductal adenocarcinoma (PDAC) and 548 healthy controls in Shanghai, China, to assess the relationships between GWAS-identified ABO alleles and risk of PDAC. Carriers of the C allele of rs505922 had an increased cancer risk [adjusted odds ratio (OR) = 1.42, 95% confidence interval (CI): 1.02-1.98] compared to TT carriers. The T alleles of rs495828 and rs657152 were also significantly associated with an elevated cancer risk (adjusted OR = 1.58, 95% CI: 1.17-2.14; adjusted OR = 1.51, 95% CI: 1.09-2.10). The rs630014 variant was not associated with risk. We did not find any significant gene-environment interaction with cancer risk using a multifactor dimensionality reduction (MDR) method. Haplotype analysis also showed that the haplotype CTTC was associated with an increased risk of PDAC (adjusted OR = 1.46, 95% CI: 1.12-1.91) compared with haplotype TGGT. GWAS-identified ABO variants are thus also associated with risk of PDAC in the Chinese population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".