Familial colorectal cancer risk by subsite of primary cancer: a population-based study in Utah
Bibliographic record
Abstract
BACKGROUND: Familial occurrence is common in colorectal cancer (CRC), but whether this increased familial risk differs by colonic subsite of the index patients CRC is not well understood. AIM: To quantify the risk of CRC in first-degree (FDR), second-degree (SDR) and first cousin (FC) relatives of individuals with CRC, stratified by subsite in the colorectum and age at diagnosis. METHODS: Colorectal cancers diagnosed between 1980 and 2010 were identified from the Utah Cancer Registry and linked to pedigrees from the Utah Population Database. Age and gender-matched CRC-free controls were selected to form the comparison group for determining CRC risk in relatives using Cox regression analysis. RESULTS: Of the 18,208 index patients diagnosed with CRC, 6584 (36.2%) were located in the proximal colon, 5986 (32.9%) in the distal colon and 5638 (31%) in the rectum. The elevated risk of CRC in relatives was similar in analysis stratified for CRC colorectal subsites in the index cases. FDR had similarly elevated risk of all site CRC, whether the index patient had cancer in the proximal colon [hazards ratio (HR): 1.85; 95% CI: 1.70-2.02], distal colon (HR: 1.90; 95% CI: 1.73-2.08) or rectum (HR: 1.83; 95% CI: 1.66-2.02) compared to relatives of controls. This risk was consistently greater for FDR when cases developed CRC below the age of 60 years. CONCLUSIONS: Relatives of CRC patients have a similarly elevated risk of CRC regardless of colonic tumour subsite in the index patient, and it is greatest for relatives of younger age index cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".