Long-term Trends in Bladder Cancer Incidence Using a Harmonized Staging Variable: A SEER-Based Study
Bibliographic record
Abstract
BACKGROUND: Coding changes in disease definitions have influenced trends in bladder cancer epidemiologic outcomes in registries. The Surveillance, Epidemiology, and End Results (SEER) Program introduced a harmonized staging variable (available from the SEER Program upon request) for long-term trend analysis. This study analyzes trends in bladder cancer incidence using the harmonized staging variable. METHODS: Using SEER 12 registry data, we compared trends in the incidence of bladder cancer cases diagnosed from 1992 to 2019 using the revised (or harmonized) staging variable against the original staging variables (SEER modified AJCC third edition for 1992-2003, Derived AJCC sixth edition for 2004-2015, Derived SEER Combined for 2016-2017, and Derived EOD 2018 for 2018-2019). We used joinpoint regression to analyze changes in trends. RESULTS: The data availability has improved with the revised staging system as the proportion of cases with missing lymph node and metastasis stages was substantially reduced. However, the trends varied by tumor stages between the two systems. There were generally more discontinuities in trends with the original system than with the revised system. Unlike the trend observed with the original staging system, the harmonized staging system has shown a 4% annual decrease in Tis incidence since 1992. CONCLUSIONS: With the revised variable, we observed a consistent decrease in the incidence of Tis cases, and the trends seem smoother. IMPACT: Our study highlights the benefits of using revised staging variables to reveal previously hidden patterns, supporting the use of new variables for a more nuanced understanding of temporal trends in epidemiology.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".