Incidence of Right-Sided Colorectal Cancer After Breast Cancer: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVES: Estrogen levels, which are involved in the development of breast cancer, may also be responsible for a higher incidence of right-sided colorectal neoplasia in women. Our objective was to determine the incidence of right-sided colorectal cancer (CRC) after the diagnosis of breast cancer. METHODS: All cases of breast cancers diagnosed between 1956 and 2006 were identified from the Manitoba Cancer Registry (MCR) and followed up until the diagnosis of any invasive cancer, death, migration out of the province, or 31 December 2006. Standardized incidence ratios (SIRs) for all CRC and right-sided CRC (cecum, ascending colon, and hepatic flexure) were calculated to compare the observed CRC incidence with that expected in the general population. Stratified analysis was performed to determine the risk at different follow-up time intervals, age at breast cancer diagnosis, and for tamoxifen use. RESULTS: There were 23,377 cases of breast cancer diagnosed between 1956 and 2006 with a total follow-up of 221,364 patient-years. The SIR for all CRC was 0.96 (95% confidence interval (CI) 0.87-1.06) and for right-sided CRC it was 1.02 (95% CI 0.87-1.20). The SIRs remained close to unity at different time intervals, for different age groups, and in analysis restricted to more recent years (1985-2006). Tamoxifen use did not alter the risk of all CRC (SIR 1.22; 95% CI 0.92-1.62) or right-sided CRC (SIR 0.90; 95% CI 0.48-1.54). CONCLUSIONS: There is no increase in the overall risk for CRC or for right-sided CRC after the diagnosis of breast cancer. CRC screening strategy for breast cancer survivors should be similar to that for the general population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".