Epidemiology of Unconventional Histological Subtypes of Urethral Cancer
Bibliographic record
Abstract
INTRODUCTION: The aim of the study was to examine cancer-specific mortality (CSM) of unconventional urethral cancers. METHODS: Within the SEER (2004-2016) database, we analyzed CSM of 165 patients with unconventional urethral-cancer histology. Kaplan-Meier plots were used to test the effect of unconventional histologies in urethral cancer on CSM. RESULTS: Of 165 eligible patients, the Mullerian type accounted for 55 (33.3%) versus melanocytic (26.7%) versus neuroendocrine 25 (15.2%) versus lymphoma 22 (13.3%) versus mesenchymal/sarcoma 15 (9.1%) versus spindle cell 4 (2.1%) patients. Median age at diagnosis was 81 years in spindle cell, 75 in melanocytic, 74 in neuroendocrine and mesenchymal/sarcoma, 67 in lymphoma, and 62 years Mullerian type (p < 0.001). Of all, 116 (70.3%) were female. The Mullerian type exhibited the highest female ratio (96.4%) versus the lowest female ratio in neuroendocrine (24.0%). The Mullerian type was most frequent in African-American females. In Caucasian females, the melanocytic type was most frequent (49.1%). In African-American (38.9%) and Caucasian males (33.3%), neuroendocrine histology was most frequent. Three-year CSM was, respectively, 27.5%, 23.1% 22.3%, 20.5%, and 16.1% for melanocytic, mesenchymal/sarcoma, Mullerian type, neuroendocrine, and lymphoma histology. Median cancer-specific survival was 106 versus 10 months for combined nonmetastatic versus metastatic nonconventional histologies. CONCLUSION: Important age, sex, racial/ethnic group distribution, and survival differences exist between each unconventional urethral-cancer histological subtypes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".