Bibliographic record
Abstract
Urothelial cancer of the bladder is a smoking-related cancer and the fifth most common cancer in the United States. At presentation, up to 25% of patients will have muscle-invasive disease and, despite cystectomy or bladder-sparing trimodality approaches, will develop metastatic disease. Cisplatin-based combination chemotherapy regimens remain the standard of care in first-line metastatic disease. Although response rates to these regimens are high, they are rarely durable, and median overall survival is only 12 to 15 months. Treatment options following progression on cisplatin-based regimens or for patients unfit for cisplatin due to poor performance status, impaired renal function, or comorbidities have been quite limited. However, there is now a new class of drugs known as immune checkpoint inhibitors, which target the programmed cell death 1/programmed cell death-ligand 1 axis and promote antitumor immunity, that are showing both efficacy and tolerability. These drugs have now been approved for use in both cisplatin-treated and most recently cisplatin-unfit patients. Clinical trials are currently ongoing to determine how best to use these drugs and whether they should be used alone or in combination with other treatments. This review will discuss the current standard of care in the management of urothelial cancer and highlight recent trials of immunotherapy in this 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 | 0.001 | 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 | 0.002 |
| 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".