Colorectal Cancer Mortality in First-Degree Relatives of Early-Onset Colorectal Cancer Cases
Bibliographic record
Abstract
PURPOSE: Estimates of familial colorectal cancer risks are useful in genetic counseling and as a guide to determining entry into screening programs and trials of chemoprevention. Furthermore, they provide an insight into the contribution of the known colorectal cancer genes to the familial risk of the disease. There is a paucity of data about the familial colorectal cancer risk associated with early-onset disease outside the recognized cancer predisposition syndromes. METHODS: This was a retrospective cohort study. The parents and siblings of 205 patients with colorectal cancer aged less than 55 years at diagnosis were studied for mortality and cancer incidence. RESULTS: The overall standardized mortality ratio of colorectal cancer compared with the Northern Irish population was 3.54 (95 percent confidence interval, 2.59-4.79). There was some evidence that a family history of colorectal cancer is associated with a greater risk of colon (4.16; 95 percent confidence interval, 2.83-5.91) rather than rectal cancer (2.62; 95 percent confidence interval, 1.43-4.40). Risks in parents (2.54; 95 percent confidence interval, 1.45-3.72) were lower than in siblings (6.15; 95 percent confidence interval, 3.90-9.23). CONCLUSION: First-degree relatives of patients with early-onset disease are at a marked increase in risk. There is evidence that risks vary depending on the type of affected relative and by the site of colorectal cancer. This information should be considered in formulating screening strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".