Emerging Immunotherapy Options for bacillus Calmette-Guérin Unresponsive Nonmuscle Invasive Bladder Cancer
Bibliographic record
Abstract
PURPOSE: Due to the high rate of recurrence and progression in patients with high risk nonmuscle invasive bladder cancer, there is an important unmet need to identify new therapies. This is particularly true for patients with recurrence after optimal intravesical bacillus Calmette-Guérin therapy, who are classified as having bacillus Calmette-Guérin unresponsive disease. MATERIALS AND METHODS: database was searched for publications related to immunotherapy for the treatment of patients with nonmuscle invasive bladder cancer who have recurrent or progressive disease despite receiving intravesical bacillus Calmette-Guérin therapy. Relevant congress abstracts were identified through searches of individual congress websites. Relevant planned and ongoing studies were identified via ClinicalTrials.gov or associated web searches. RESULTS: We provide a summary of the currently available immunotherapy options for patients with bacillus Calmette-Guérin unresponsive nonmuscle invasive bladder cancer, and discuss planned and ongoing research of potential targeted agents and immunotherapy based combination regimens. CONCLUSIONS: There is a clear biological and clinical rationale for the continued evaluation of immune based therapies in the setting of bacillus Calmette-Guérin unresponsive nonmuscle invasive bladder cancer. Data from early phase trials with novel immunotherapies targeting multiple immune related pathways have emerged, which support additional studies to assess the benefits of immune checkpoint inhibitors and other immunotherapy based regimens for patients with bacillus Calmette-Guérin unresponsive nonmuscle invasive bladder cancer.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".