Comparative effectiveness of neoadjuvant chemotherapy in bladder and upper urinary tract urothelial carcinoma
Bibliographic record
Abstract
OBJECTIVE: To assess the differential response to neoadjuvant chemotherapy (NAC) in patients with urothelial carcinoma of the bladder (UCB) compared to upper tract urothelial carcioma (UTUC) treated with radical surgery. PATIENTS AND METHODS: Data from 1299 patients with UCB and 276 with UTUC were obtained from multicentric collaborations. The association of disease location (UCB vs UTUC) with pathological complete response (pCR, defined as a post-treatment pathological stage ypT0N0) and pathological objective response (pOR, defined as ypT0-Ta-Tis-T1N0) after NAC was evaluated using logistic regression analyses. The association with overall (OS) and cancer-specific survival (CSS) was evaluated using Cox regression analyses. RESULTS: A pCR was found in 250 (19.2%) patients with UCB and in 23 (8.3%) with UTUC (P < 0.01). A pOR was found in 523 (40.3%) patients with UCB and in 133 (48.2%) with UTUC (P = 0.02). On multivariable logistic regression analysis, patients with UTUC were less likely to have a pCR (odds ratio [OR] 0.45, 95% confidence interval [CI] 0.27-0.70; P < 0.01) and more likely to have a pOR (OR 1.57, 95% CI 1.89-2.08; P < 0.01). On univariable Cox regression analyses, UTUC was associated with better OS (hazard ratio [HR] 0.80, 95% CI 0.64-0.99, P = 0.04) and CSS (HR 0.63, 95% CI 0.49-0.83; P < 0.01). On multivariable Cox regression analyses, UTUC remained associated with CSS (HR 0.61, 95% CI 0.45-0.82; P < 0.01), but not with OS. CONCLUSIONS: Our present findings suggest that the benefit of NAC in UTUC is similar to that found in UCB. These data can be used as a benchmark to contextualise survival outcomes and plan future trial design with NAC in urothelial cancer.
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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 | 0.002 | 0.008 |
| 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) | 0.001 | 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".