Prognostic Factors for Patients With Urachal Carcinoma Undergoing Radical Surgery: Risk Stratification for Future Prospects of Precision Oncology
Bibliographic record
Abstract
Purpose: To determine poor prognostic factors for patients with urachal carcinoma (UrC) undergoing radical surgery; identify candidates for precision oncology, including adjuvant therapy; and improve survival outcome of this rare malignant disease. Materials and Methods: We included 51 patients with UrC who underwent radical or partial cystectomy at our institution between 1991 and 2023. Kaplan-Meier curves and log-rank test were performed to estimate overall survival (OS) and recurrence-free survival by applying the Ontario staging system. A Cox proportional hazard regression model was used for multivariate analysis to evaluate prognostic factors for patients undergoing radical surgery. Results: Univariate and multivariate analyses showed that tumor involvement of perivesical fat (Ontario stage T3) and tumor grade were significant prognostic factors for OS. Tumor involvement of perivesical fat was a common factor for both OS and recurrence-free survival. Patients with both adverse factors showed significantly poor OS compared with those with 1 or no adverse factors ( P = .014 and .0014, respectively). Conclusions: Tumor involvement of perivesical fat and tumor grade were strong predictors of survival outcome. Adjuvant therapy might be indicated in patients with high recurrence risk. Our results warrant further, multidisciplinary investigation into the impact of precision oncology for patients with UrC and high recurrence risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".