dbCPCO: a database of genetic markers tested for their predictive and prognostic value in colorectal cancer
Bibliographic record
Abstract
Colorectal cancer is the third most common cancer with a 5-year survival rate of 30-65%. A portion of the interpatient variability in its clinical outcome is attributed to inherited and somatic genetic factors. Although numerous research articles have investigated these factors in colorectal cancer, there has not been a central resource, such as a public database, that compiles these findings. Here we describe the dbCPCO, a database of genetic variations tested for association with colorectal cancer prognosis and clinical outcome. dbCPCO curates the results of research articles on colorectal cancer that investigate the possible correlation of genetic factors with various patient and tumor characteristics. Literature reports are retrieved from PubMed. The data that meet the inclusion criteria are compiled in a relational database and posted in a dedicated Website. The genetic factors include inherited genetic polymorphisms, and somatic and germline mutations in both nuclear and mitochondrial DNA. As of March 2010, the dbCPCO Website posts 778 scientific findings on 456 polymorphisms, somatic and germline mutations from 189 genes, and genetic loci tested for correlation with clinicopathological features and/or clinical outcome in colorectal cancer. The dbCPCO is periodically updated and freely available for the scientific and medical community at http://www.med.mun.ca/cpco.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".