Urothelial Cancer and the Diagnosis of Subsequent Malignancies
Bibliographic record
Abstract
PURPOSE: We examine the likelihood of a second primary malignancy diagnosis following the diagnosis of urothelial cancer. METHODS: We identified subjects from the Manitoba Cancer Registry diagnosed with urothelial cancer between April 1, 1985 and December 31, 2007. Data were collected on all subsequent new cancer diagnoses. Standardized incidence ratios (SIRs) were calculated for each major cancer type, matched with the general population by age, sex and period. Further analysis was undertaken stratifying by morphology and invasiveness. The results in males were examined with and without prostate cancer. A competing risk model was used to analyze the data controlling for death. RESULTS: Of the 4412 included urothelial cancer cases, 712 patients (16.1%) subsequently developed a second primary malignancy. Risks were highest within 1 year of diagnosis persisting for 5 years. This risk was highest in males aged less than 70 (SIR = 6.25; 95% Confidence Interval [CI] 5.08-7.04). Overall, the risk was similar between the sexes (female SIR: 1.30, CI 1.09-1.54; males 1.42, CI 1.31-1.54; males excluding prostate SIR: 1.22 CI 1.11-1.35). There was an increased relative risk for developing a second primary for cancers of the kidney (male), lung, breast (female) and prostate. Papillary cancers were associated with increased relative risk of developing lung, prostate, and breast (female and male) cancer. In the competing risks model, patients diagnosed with a papillary or in situ urothelial cancer were more likely to be diagnosed with a second primary than non-papillary and invasive disease, respectively. CONCLUSIONS: Those diagnosed with urothelial cancer have an increased probability of having a second primary cancer detected within the subsequent 5 years, even when prostate cancer is excluded. Papillary tumours in particular may provide a warning for subsequent malignancy.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".