The Impact of Variant Histology in Patients with Urothelial Carcinoma Treated with Radical Cystectomy: Can We Predict the Presence of Variant Histology?
Bibliographic record
Abstract
Considering the divergent biological behaviors of certain histological subtypes of urothelial carcinoma, it would be of great importance to examine the impact of variant histology and to predict its presence in patients with bladder cancer. A single-center cohort study included 459 patients who underwent radical cystectomy for urothelial carcinoma between 2017 and 2021. Patients were followed up with until July 2022. We compared clinical, laboratory, and histopathologic characteristics and the overall survival between patients with pure urothelial carcinoma and variant histologies. Our results showed that the patients with variant histology were older and preoperatively more frequently had hydronephrosis and higher values of leukocytes and neutrophils. Also, we found a significant association between variant histology and an advanced stage of tumor disease, the presence of lymphovascular invasion, positive surgical margins, and metastases in surgically resected lymph nodes. The number of neutrophils was identified as an independent preoperative predictor of the presence of variant histology after a radical cystectomy. The overall survival of the patients with variant histology was significantly lower compared to the patients with pure urothelial carcinoma. According to our results, the presence of variant histology represents a more aggressive form of the disease. Preoperative neutrophil counts may indicate the presence of variant histology of urothelial carcinoma in patients with bladder cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".