The Effect of Systemic Chemotherapy on Survival in Patients With Localized, Regional, or Metastatic Adenocarcinoma of the Urinary Bladder
Bibliographic record
Abstract
OBJECTIVES: To test the effect of systemic chemotherapy on cancer-specific mortality (CSM) in patients with adenocarcinoma of the urinary bladder (ADKUB). MATERIALS AND METHODS: Within the Surveillance, Epidemiology, and End Results registry (2004 to 2016), we identified patients with localized (T2-3N0M0), regional (T4N0M0/TanyN1-3M0), and metastatic (TanyNanyM1) ADKUB. Temporal trends, Kaplan-Meier plots, and multivariable Cox regression models were used before and after 1:1 propensity score matching and inverse probability of treatment weighting. RESULTS: Of 1537 patients with ADKUB, 834 (54.0%), 363 (23.5%), and 340 (22.5%) harbored localized, regional, and metastatic disease, respectively. The rates of chemotherapy use increased in localized (estimated annual percentage change [EAPC]: +2.7%; P=0.03) and regional ADKUB (EAPC: +2.4%; P=0.04). Conversely, chemotherapy rates remained stable in metastatic patients (EAPC: +1.6%; P=0.4). In multivariable Cox regression models, chemotherapy use was associated with lower CSM in metastatic ADKUB (hazard ratio [HR]: 0.5; P=0.003), but not in either localized (HR: 0.8; P=0.2) or in regional ADKUB (HR: 1.0; P=0.9). In metastatic ADKUB, the benefit of chemotherapy on CSM persisted after 1:1 propensity score matching (HR: 0.6; P=0.002) and after inverse probability of treatment weighting (HR: 0.4; P<0.001). CONCLUSIONS: Chemotherapy improves survival in metastatic ADKUB. However, only one out of 2 such patients benefit from chemotherapy. In consequence, greater emphasis on chemotherapy use may be warranted in these patients. Conversely, no benefit was identified in localized or regional ADKUB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".