Do Histology and Primary Tumor Location Influence Metastatic Patterns in Bladder Cancer?
Bibliographic record
Abstract
Metastasis is the leading cause of death in patients with bladder cancer. This study utilized a statistical analysis of patient data from the Surveillance, Epidemiology, and End Results database to examine the influence of histological type and primary site on the metastatic behavior of bladder cancer. Significantly different metastatic patterns were observed among bladder cancer patients depending on their histological type. Patients with squamous cell carcinoma showed a significantly (p < 0.001) lower bone metastasis rate (27.2%) than patients with urothelial carcinoma (UC) (38.3%). Patients with neuroendocrine carcinoma showed a significantly (p < 0.001) higher liver metastasis rate (52.1%) and a significantly (p = 0.001) lower lung metastasis rate (25.7%) than patients with UC (22.6% and 33.5%, respectively). UC patients also demonstrated differences in metastatic behavior according to histological subtype. The sarcomatoid subtype showed a significantly (p < 0.001) higher lung metastasis rate (51.6%) and a significantly lower (p = 0.002) lymph node metastasis rate (22.6%) than the micropapillary subtype (12.1% and 54.1%, respectively). Significant differences in metastatic behavior were also observed among patients with conventional UCs originating from the bladder, ureter, and renal pelvis. This study highlights the impact of histological characteristics and primary site on metastatic tendencies in bladder cancer, highlighting the importance of tailoring treatment and surveillance strategies.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| 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".