Optimizing management of advanced urothelial carcinoma: A review of emerging therapies and biomarker-driven patient selection
Bibliographic record
Abstract
INTRODUCTION: Advanced urothelial carcinoma has been challenging to treat due to limited treatment options, poor response rates, and poor long-term survival. New treatment options hold the promise of improved outcomes for these patients. METHODS: A multidisciplinary working group drafted a management algorithm for advanced urothelial carcinoma using "consensus development conference" methodology. A targeted literature search identified new and emerging treatments for inclusion in the management algorithm. Published clinical data were considered during the algorithm development process, as well as the risks and benefits of the treatment options. Biomarkers to guide patient selection in clinical trials for new treatments were incorporated into the algorithm. RESULTS: The advanced urothelial carcinoma management algorithm includes newly approved first-line anti-programmed death receptor-1 (PD1)/ programmed death-ligand 1 (PD-L1) therapies, a newly approved anti-fibroblast growth factor receptors (FGFR) therapy, and an emerging anti-Nectin 4 therapy, which have had encouraging results in phase 2 trials for second-line and third-line therapy, respectively. This algorithm also incorporates suggestions for biomarker testing of PD-L1 expression and FGFR gene alterations. CONCLUSIONS: Newly approved and emerging therapies are starting to cover an unmet need for more treatment options, better response rates, and improved overall survival in advanced urothelial carcinoma. The management algorithm provides guidance on how to incorporate these new options, and their associated biomarkers, into clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".