Histologic variants of urothelial bladder cancer and nonurothelial histology in bladder cancer
Bibliographic record
Abstract
Bladder cancer can be classified histologically as urothelial or non-urothelial. Urothelial cancer has a propensity for divergent differentiation, which has increasingly been recognized in recent years due to heightened awareness and improved immunohistochemistry techniques. Furthermore, the recent World Health Organization classification of urothelial cancers improved clarity on this issue, with its listing of 13 histologic variants of urothelial cancer. The divergent differentiation patterns include, amongst others, squamous, glandular, micropapillary, nested, lymphepithelioma-like, plasmacytoid and sarcomatoid variants of urothelial cancer. Attempts to quantify the amount of divergent differentiation present, such as using the nonconventional differentiation number, have been made recently, which will improve the ability to compare publications from different centres. Genetic-based studies have indicated that the histologic variants of urothelial cancer arise from a common clonal precursor. Mostly, the current evidence suggests that urothelial cancer with divergent differentiation has a worse prognosis when compared with pure urothelial cancer. This article will review the current literature on variant histologies of urothelial cancer, and well as new developments in pure squamous cell carcinoma, small cell carcinoma and adenocarcinoma of the bladder.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".