Bibliographic record
Abstract
Although immune-mediated therapies have been used in genitourinary (gu) malignancies for decades, recent advances with monoclonal antibody checkpoint inhibitors (cpis) have led to a number of promising treatment options. In renal cell carcinoma (rcc), cpis have been shown to have benefit over conventional therapies in a number of settings, and they are the standard of care for many patients with metastatic disease. Based on recent data, combinations of cpis and antiangiogenic therapies are likely to become a new standard approach in rcc. In urothelial carcinoma, cpis have been shown to have a role in the second-line treatment of metastatic disease, and a number of clinical trials are actively investigating cpis for other indications. In other gu malignancies, such as prostate cancer, results to date have been less promising. Immunotherapies continue to be an area of active study for all gu disease sites, with several clinical trials ongoing. In this review, we summarize the current evidence for cpi use in rcc, urothelial carcinoma, prostate cancer, testicular germ-cell tumours, and penile carcinoma. Ongoing clinical trials of interest are highlighted, as are the challenges that clinicians and patients will potentially face as immune cpis become a prominent feature in the treatment of gu cancers.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".