The impact of histological variants on bladder cancer survival: A population-based analysis.
Bibliographic record
Abstract
458 Background: We evaluated the clinical and prognostic impact of bladder cancer histologic variants (BCHV) using a large population-based cancer database. Methods: Using the Surveillance, Epidemiology, and End Results database (SEER), we identified bladder cancer patients from 2001-2012, and categorized them according to histological differentiation. 5 year disease-specific survival (DSS) was calculated using the Kaplan-Meier method. Cox proportional hazards regression model was used to predict association with disease-specific mortality (DSM). In addition, we fitted multivariate logistic regression models to predict the impact of histological variants on muscle-invasive status (MI), nodal involvement (NI), and metastatic disease (MD). Results: The cohort included 175,544 urothelial (96.3%) and 6,714 non-urothelial (3.7%) cancers. The latter were divided into: 2,382 squamous cell carcinoma, 1,648 adenocarcinoma, 888 small cell, 912 sarcomatoid, 292 signet-ring cell, 314 neuroendocrine and 278 micropapillary bladder tumors. Urothelial cancers overall had the best 5-year DSS. Of the non-urothelial variants, micropapillary and squamous had the best and worst DSS respectively (p < 0.001). On multivariable analysis predicting DSM, micropapillary and squamous variants had the best and worst prognosis respectively (HR 0.79, p = 0.102 and HR 2.63, p < 0.001), compared to urothelial tumors. On multivariable analysis predicting MI, NI, and MD: squamous (OR 22.76, p < 0.001), micropapillary (OR 3.17, p < 0.001) and adenocarcinoma (OR 4.14, p < 0.001), had higher likelihood respectively, compared to urothelial tumors. Conclusions: Despite accounting for a minority of bladder cancers, BCHV are associated with worst outcomes. It is essential to recognize the potential implications of these variants when deciding treatment. Additional studies are warranted to better characterize the clinical impact of these variants.
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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 | 0.002 |
| 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 | 0.000 |
| 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".