Contemporary rates of pathological features and mortality for adenocarcinoma of the urinary bladder in the USA
Bibliographic record
Abstract
OBJECTIVES: To examine contemporary rates of pathological features and mortality for adenocarcinoma of the urinary bladder in the USA using population-based data analysis. METHODS: We relied on 10 024 patients with non-metastatic bladder cancer diagnosed between 2004 and 2013 within the Surveillance, Epidemiology and End Results registries. Logistic regression analyses focused on grade and stage. Kaplan-Meier analyses assessed cancer-specific mortality rates in adenocarcinoma and urothelial carcinoma of the bladder. Cox regression analyses assessed the impact of histological subtype on cancer-specific mortality. RESULTS: Overall, 215 (2.1%) adenocarcinoma and 9809 (97.9%) urothelial carcinoma patients were identified. The rate of non-organ-confined disease was higher in adenocarcinoma (64.7% vs 50.8%, P < 0.001). In multivariable logistic regression analyses, adenocarcinoma patients had a 2.2-fold higher risk of harboring non-organ-confined disease (95% confidence interval 1.7-3.0; P < 0.001) than urothelial carcinoma patients. Cancer-specific mortality-free survival rates were lower in adenocarcinoma (P < 0.01). This disadvantage only applied to non-organ-confined disease (P = 0.044), and not to organ-confined disease (P = 0.9). In multivariable Cox regression analyses, adenocarcinoma conferred a 1.3-fold higher rate of cancer-specific mortality (hazard ratio 1.30, 95% confidence interval 1.05-1.60; P = 0.01). Among adenocarcinoma patients, 30.7% harbored signet-ring cell adenocarcinoma and portended particularly poor cancer-specific mortality rates. CONCLUSIONS: In bladder cancer, adenocarcinoma presents at higher stages than urothelial carcinoma. However, cancer-specific mortality rates do not differ. A more unfavorable stage at diagnosis and higher cancer-specific mortality apply to the signet-ring cell variant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".