Bladder and upper urinary tract cancers as first and second primary cancers
Bibliographic record
Abstract
BACKGROUND: Previous population-based studies on second primary cancers (SPCs) in urothelial cancers have focused on known risk factors in bladder cancer patients without data on other urothelial sites of the renal pelvis or ureter. AIMS: To estimate sex-specific risks for any SPCs after urothelial cancers, and in reverse order, for urothelial cancers as SPCs after any cancer. Such two-way analysis may help interpret the results. METHODS: We employed standardized incidence ratios (SIRs) to estimate bidirectional relative risks of subsequent cancer associated with urothelial cancers. Patient data were obtained from the Swedish Cancer Registry from years 1990 through 2015. RESULTS: We identified 46 234 urinary bladder cancers (75% male), 940 ureteral cancers (60% male), and 2410 renal pelvic cancers (57% male). After male bladder cancer, SIRs significantly increased for 9 SPCs, most for ureteral (SIR 41.9) and renal pelvic (17.2) cancers. In the reversed order (bladder cancer as SPC), 10 individual FPCs were associated with an increased risk; highest associations were noted after renal pelvic (21.0) and ureteral (20.9) cancers. After female bladder cancer, SIRs of four SPCs were significantly increased, most for ureteral (87.8) and pelvic (35.7) cancers. Female bladder, ureteral, and pelvic cancers associated are with endometrial cancer. CONCLUSIONS: The risks of recurrent urothelial cancers were very high, and, at most sites, female risks were twice over the male risks. Risks persisted often to follow-up periods of >5 years, motivating an extended patient follow-up. Lynch syndrome-related cancers were associated with particularly female urothelial cancers, calling for clinical vigilance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".