Regional differences in metastatic urothelial carcinoma of the urinary bladder patients across the United States SEER registries
Bibliographic record
Abstract
INTRODUCTION: Despite advances in treatment, metastatic urothelial carcinoma of the urinary bladder (mUCUB) is associated with high mortality and treatment risk. We tested for regional differences in mUCUB within a large-scale, population-based database. METHODS: Using the Surveillance, Epidemiology and End Results (SEER) database (2010-2018), patient (age, sex, race/ethnicity), tumor (T-stage, N-stage, number of metastatic sites), and treatment (systemic therapy, radical cystectomy) characteristics were tabulated for mUCUB patients according to 11 SEER registries. Multinomial regression models and multivariable Cox regression models tested overall mortality (OM), adjusting for patient, tumor and treatment characteristics. RESULTS: In 4817 mUCUB patients, registry-specific patient counts ranged from 1855 (38.5%) to 105 (2.2%). Important inter-regional differences existed for race/ethnicity (3-36% for others than non-Hispanic Whites), N-stage (28-39% for N1-3, 44-58% in N0, 8-22% for unknown N-stage), systemic therapy (38-54%) and radical cystectomy (3-11%). In multivariable analyses adjusting for these patient, tumor, and treatment characteristics, one registry exhibited significantly lower OM (SEER registry 10: hazard ratio [HR] 0.83) and two other registries exhibited significantly higher OM (SEER registries 9: HR 1.13; SEER registry 8: HR 1.24) relative to the largest reference registry (n=1855). CONCLUSIONS: We identified important regional differences that included patient, tumor, and treatment characteristics. Even after adjustment for these characteristics, important OM differences persisted, which may warrant more detailed investigation.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".