Predictive value of mitotic figure counts in tumor progression of non-invasive high-grade papillary urothelial carcinoma of the urinary bladder: A retrospective study from a single cancer center
Bibliographic record
Abstract
Background: Urothelial carcinoma (UC) is the most common type of bladder malignancy. Although the majority of UC present as non-invasive tumors, a subset of them progress into invasive cancer and cause significant morbidity and mortality. Objective: In this study, we examined the association between tumor mitotic activity associated and the progression of non-invasive high-grade papillary UC of the bladder. Methods: Forty-four cases of tumors that met the selection criteria were retrieved from the Department of Pathology archives, and, for each case, mitotic figures were counted in 10 high-power fields (HPF) by two independent pathologists. Tumor progression was defined as the invasion of the tumor into the subepithelial connective tissue (lamina propria) or beyond during follow-ups. In addition, tumors that later exhibited distant metastases were included in the tumor progression group. Results: 0.001) than in the progression-free group. Furthermore, tumors with more than three mitotic counts per HPF in initial biopsies posed a high risk of tumor progression within the 19.5 ± 6.1 months of follow-ups. Conclusion: The findings of our study provided valuable information for further stratification of risk factors among patients with non-invasive high-grade papillary UC of the bladder. Patients with high mitotic figure count in their initial biopsies should be monitored closely or treated earlier to prevent their tumors from progressing into invasive carcinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".