A genome wide association study on Newfoundland colorectal cancer patients’ survival outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In this study we performed genome-wide association studies to identify candidate SNPs that may predict the risk of disease outcome in colorectal cancer. METHODS: Patient cohort consisted of 505 unrelated patients with Caucasian ancestry. Germline DNA samples were genotyped using the Illumina® human Omni-1quad SNP chip. Associations of SNPs with overall and disease free survivals were examined primarily for 431 patients with microsatellite instability-low (MSI-L) or stable (MSS) colorectal tumors using Cox proportional hazards method adjusting for clinical covariates. Bootstrap method was applied for internal validation of results. As exploratory analyses, association analyses for the colon (n = 334) and rectal (n = 171) cancer patients were also performed. RESULTS: As a result, there was no SNP that reached the genomewide significance levels (p < 5x10(-8)) in any of the analyses. A small number of genetic markers (n = 10) showed nominal associations (p <10(-6)) for MSS/MSI-L, colon, or rectal cancer patient groups. These markers were located in two non-coding RNA genes or intergenic regions and none were amino acid substituting polymorphisms. Bootstrap analysis for the MSS/MSI-L cohort data suggested the robustness of the observed nominal associations. CONCLUSIONS: Likely due to small number of patients, our study did not identify an acceptable level of association of SNPs with outcome in MSS/MSI-L, colon, or rectal cancer patients. A number of SNPs with sub-optimal p-values were, however, identified; these loci may be promising and examined in other larger-sized patient cohorts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it