Options in metastatic urothelial cancer after first-line therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: The treatment of patients with metastatic urothelial carcinoma is evolving with recent advances holding promise for improved outcomes. Historically, patients with metastatic urothelial carcinoma had a poor prognosis with no standard treatment options in the second-line setting. Currently, with an increased understanding of the heterogeneity of clinical bladder cancer subtypes and molecular diversity of the disease, there is optimism that outcomes will start to improve. The present review will evaluate historical second-line treatment options and focus on emerging therapies in this setting. RECENT FINDINGS: Single-agent cytotoxic chemotherapy agents continue to be evaluated in patients with metastatic urothelial carcinoma with variable results. Targeted therapies, including tyrosine kinase inhibitors and monoclonal antibodies, have also been extensively evaluated in this disease. Early phase data have as yet failed to demonstrate improvements in survival; however evaluations of targeted agents in enriched patient populations and in combination with chemotherapeutic agents are ongoing. Novel immunotherapeutic approaches have shown encouraging results and are currently being evaluated extensively. SUMMARY: The optimal treatment for patients with metastatic urothelial carcinoma in the second-line setting is unknown. Recent advances in the field gives rise to optimism as the focus shifts to individualization of therapy based on clinical and molecular characteristics of the patient and the disease.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".