Risk stratification of pT1-3N0 patients after radical cystectomy for adjuvant chemotherapy counselling
Bibliographic record
Abstract
BACKGROUND: In pT1-T3N0 urothelial carcinoma of the bladder (UCB) patients, multi-modal therapy is inconsistently recommended. The aim of the study was to develop a prognostic tool to help decision-making regarding adjuvant therapy. METHODS: We included 2145 patients with pT1-3N0 UCB after radical cystectomy (RC), naive of neoadjuvant or adjuvant therapy. The cohort was randomly split into development cohort based on the US patients (n=1067) and validation cohort based on the Europe patients (n=1078). Predictive accuracy was quantified using the concordance index. RESULTS: With a median follow-up of 45 months, 5-year recurrence-free and cancer-specific survival estimates were 68% and 73%, respectively. pT-stage, ge, lymphovascular invasion, and positive margin were significantly associated with both disease recurrence and cancer-specific mortality (P-values ≤ 0.005). The accuracies of the multivariable models at 2, 5, and 7 years for predicting disease recurrence were 67.4%, 65%, and 64.4%, respectively. Accuracies at 2, 5, and 7 years for predicting cancer-specific mortality were 69.3%, 66.4%, and 65.5%, respectively. We developed competing-risk, conditional probability nomograms. External validation revealed minor overestimation. CONCLUSION: Despite RC, a significant number of patients with pT1-3N0 UCB experience disease recurrence and ultimately die of UCB. We developed and externally validated competing-risk, conditional probability post-RC nomograms for prediction of disease recurrence and cancer-specific mortality.
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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 | 0.004 |
| 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) | 0.001 | 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".