The emerging treatment landscape of advanced urothelial carcinoma
Bibliographic record
Abstract
PURPOSE OF REVIEW: Urothelial carcinoma (UC) is one of the most common malignancies in the Western world. Historically, patients with advanced disease have had a poor prognosis and progress within months of completing upfront platinum-based chemotherapy. In the last two years, the treatment landscape for metastatic UC (mUC) has significantly shifted with the emergence of contemporary immunotherapy and targeted agents. The purpose of this review is to highlight the current and emerging systemic treatment options for mUC of the bladder. RECENT FINDINGS: PD-1/PD-L1 immune checkpoint inhibitors (ICIs) have demonstrated activity in the postplatinum and platinum-ineligible settings. Additionally, they have become a standard maintenance treatment option after avelumab demonstrated increased overall survival in patients with stable disease or better after first line platinum-based chemotherapy. Novel targeted therapies and antibody-drug conjugates (ADCs) have been granted Food and Drug Administration approval for subsequent line therapy based on promising results in phase II and III trials. SUMMARY: There has been a considerable increase in the variety of effective therapies for mUC, including the utility of ICIs, novel targeted agents, and ADCs. Platinum-based chemotherapy remains an effective first-line option. As the role of novel therapies continues to shift toward earlier in the disease course, there remains an important need to develop feasible, globally accessible predictive biomarkers that can aid in patient selection and inform sequencing of therapeutic options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".