Comparison Between Urothelial and Non-Urothelial Urethral Cancer
Bibliographic record
Abstract
To test the effect of variant histology relative to urothelial histology on stage at presentation, cancer specific mortality (CSM), and overall mortality (OM) after chemotherapy use, in urethral cancer.Within the Surveillance, Epidemiology and End Results (2004-2016) database, we identified 1,907 primary variant histology urethral cancer patients. Kaplan-Meier plots, Cox regression analyses, cumulative incidence-plots, multivariable competing-risks regression models and propensity score matching for patient and tumor characteristics were used.Of 1,907 eligible urethral cancer patients, urothelial histology affected 1,009 (52.9%) vs. squamous cell carcinoma (SCC) 455 (23.6%) vs. adenocarcinoma 278 (14.6%) vs. other histology 165 (8.7%) patients. Urothelial histological patients exhibited lower stages at presentation than SCC, adenocarcinoma or other histology patients. In urothelial histology patients, five-year CSM was 23.5% vs. 34.4% in SCC [Hazard Ratio (HR) 1.57] vs. 40.7% in adenocarcinoma (HR 1.69) vs. 43.4% in other histology (HR 1.99, p < 0.001). After matching in multivariate competing-risks regression models, variant histology exhibited 1.35-fold higher CSM than urothelial. Finally, in metastatic urethral cancer, lower OM was recorded after chemotherapy in general, including metastatic adenocarcinoma and other variant histology subtypes, except metastatic SCC.Adenocarcinoma, SCC and other histology subtypes affect fewer patients than urothelial histology. Presence of variant histology results in higher CSM. Finally, chemotherapy for metastatic urethral cancer improves survival in adenocarcinoma and other variant histology subtypes, but not in SCC.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".