A Survival Association Study of 102 Polymorphisms Previously Associated with Survival Outcomes in Colorectal Cancer
Bibliographic record
Abstract
Several published studies identified associations of a number of polymorphisms with a variety of survival outcomes in colorectal cancer. In this study, we aimed to explore 102 previously reported common genetic polymorphisms and their associations with overall survival (OS) and disease-free survival (DFS) in a colorectal cancer patient cohort from Newfoundland (n = 505). Genotypes were obtained using a genomewide SNP genotyping platform. For each polymorphism, the best possible genetic model was estimated for both overall survival and disease-free survival using a previously published approach. These SNPs were then analyzed under their genetic models by Cox regression method. Correction for multiple comparisons was performed by the False Discovery Rate (FDR) method. Univariate analysis results showed that RRM1-rs12806698, IFNGR1-rs1327474, DDX20-rs197412, and PTGS2-rs5275 polymorphisms were nominally associated with OS or DFS (p < 0.01). In stage-adjusted analysis, the nominal associations of DDX20-rs197412, PTGS2-rs5275, and HSPA5-rs391957 with DFS were detected. However, after FDR correction none of these polymorphisms remained significantly associated with the survival outcomes. We conclude that polymorphisms investigated in this study are not associated with OS or DFS in our colorectal cancer patient cohort.
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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 | 0.000 |
| 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 | 0.000 | 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".