Prognostic factors of cancer recurrence and progression in non-muscle-invasive urothelial carcinoma: A multicenter study of over 4,300 patients.
Bibliographic record
Abstract
249 Background: The outcomes of patients with non-muscle-invasive urothelial carcinoma of the bladder (NMIUCB) remain poorly understood. The aim of our study was to identify prognostic factors of cancer recurrence and progression in patients with primary UCB. Methods: We performed a combined analysis on individual data from 4,325 patients with primary NMIUCB. Results: Within a median follow-up of 64 months, 1,960 patients (45.4%) experienced disease recurrence, 498 (11.5%) experienced progression to muscle-invasive stage, 1,155 (26.7%) died of any cause, and 310 (7.2%) died of their cancer. In multivariable Cox regression analysis, advanced age, higher grade, larger tumor size, higher number of tumors, number of prior recurrences, and type of intravesical therapy were independent predictors of disease recurrence and progression. While treatment intravesical chemotherapy was only associated with decreased/delayed cancer recurrence, intravesical BCG therapy was associated with decreased/delayed cancer recurrence and progression. The predictive accuracies of the models for recurrence and progression were 63.5% and 71.3%, respectively. Conclusions: Even in a heterogenous patient population, BCG therapy appears to decrease frequency and delay time to cancer recurrence and progression in patients with NMIUCB. Predictive tools based on combination of multiple clinical variables which capture the biological and clinical potential of nonmuscle-invasive disease could help with patient counseling and individualized risk assessment for adjuvant intravesical therapy and clinical trial design. [Table: see text] No significant financial relationships to disclose.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".