The Risk of Colorectal Cancer Is Not Increased after a Diagnosis of Urothelial Cancer: A Population-Based Study
Bibliographic record
Abstract
Background The data about whether patients with a prior urothelial cancer (UCa) are at increased risk of colorectal cancer (CRC) are conflicting. We used a competing risks analysis to determine the risk of CRC after UCa. Methods Historical cohorts were assembled by record linkage of Manitoba Cancer Registry and Manitoba Health databases. The incidence of CRC for individuals with UCa as their first cancer between 1987 and 2009 was compared with the incidence for randomly selected age- and sex-matched individuals without a cancer diagnosis at the index date (UCa diagnosis date). Three competing outcomes (CRC, another primary cancer, and death) were evaluated by competing risks proportional hazards models with adjustment for relevant confounders. Results The cohorts of 4591 patients with UCa and 22,312 without UCa were followed for a total of 179,287 person– years (py). After UCa, the rate of subsequent colon cancer in UCa patients was 4.5 per 1000 py compared with 3.6 per 1000 py in the non-cancer cohort. In the multivariable analysis, no overall increase in CRC risk was observed for patients first diagnosed with UCa (hazard ratio: 0.88; 95% confidence interval: 0.70 to 1.1; p = 0.26). Conclusions Because of similar CRC risk, a similar CRC screening strategy should be applied for individuals with and without UCa.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".