Trimodal therapy effect on survival in urothelial vs non‐urothelial bladder cancer
Bibliographic record
Abstract
OBJECTIVE: To address cancer-specific mortality free-survival (CSM-FS) differences in patients with urothelial carcinoma of the urinary bladder (UCUB) vs non-UCUB who underwent trimodal therapy (TMT), according to organ confined (OC: T2N0M0) vs non-organ confined (NOC: T3-4NanyM0 or TanyN1-3M0) clinical stages. PATIENTS AND METHODS: Within the Surveillance, Epidemiology, and End Results database (2004-2020), we identified patients with cT2-T4N0-N3M0 bladder cancer treated with TMT, defined as the combination of transurethral resection of bladder tumour, chemotherapy, and radiotherapy. Temporal trends described TMT use over time. Kaplan-Meier plots and multivariable Cox regression (MCR) models addressed CSM in UCUB vs non-UCUB according to OC vs NOC stages. RESULTS: Of 5130 assessable TMT-treated patients, 425 (8%) harboured non-UCUB vs 4705 (92%) who had UCUB. The TMT rates increased for patients with OC UCUB from 92.4% to 96.8% (estimated annual percentage change of 0.4%, P < 0.001), but not in the NOC stages (P = 0.3). In the OC stage, the median CSM-FS was 36 months in patients with non-UCUB vs 60 months in those with UCUB, respectively (P = 0.01). Conversely, in the NOC stage, the median CSM-FS was 23 months both in UCUB and non-UCUB (P = 0.9). In the MCR models addressing OC stage, non-UCUB histology independently predicted higher CSM (hazard ratio 1.45, P = 0.004), but not in the NOC stage (P = 0.9). CONCLUSION: In OC UCUB, TMT rates have increased over time in a guideline-consistent fashion. Patients with OC non-UCUB treated with TMT showed a CSM disadvantage relative to OC UCUB. In the NOC stage, use of TMT resulted in dismal CSM, regardless of UCUB vs non-UCUB histology.
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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) | 0.000 | 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) | 0.002 | 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".