Definitions, End Points, and Clinical Trial Designs for Bladder Cancer: Recommendations From the Society for Immunotherapy of Cancer and the International Bladder Cancer Group
Bibliographic record
Abstract
PURPOSE: There is a significant unmet need for new and efficacious therapies in urothelial cancer (UC). To provide recommendations on appropriate clinical trial designs across disease settings in UC, the Society for Immunotherapy of Cancer (SITC) and the International Bladder Cancer Group (IBCG) convened a multidisciplinary, international consensus panel. METHODS: Through open communication and scientific debate in small- and whole-group settings, surveying, and responses to clinical questionnaires, the consensus panel developed recommendations on optimal definitions of the disease state, end points, trial design, evaluations, sample size calculations, and pathology considerations for definitive studies in low- and intermediate-risk nonmuscle-invasive bladder cancer (NMIBC), high-risk NMIBC, muscle-invasive bladder cancer in the neoadjuvant and adjuvant settings, and metastatic UC. The expert panel also solicited input on the recommendations through presentations and public discussion during an open session at the 2021 Bladder Cancer Advocacy Network (BCAN) Think Tank (held virtually). RESULTS: The consensus panel developed a set of stage-specific bladder cancer clinical trial design recommendations, which are summarized in the table that accompanies this text. CONCLUSION: These recommendations developed by the SITC-IBCG Bladder Cancer Clinical Trial Design consensus panel will encourage uniformity among studies and facilitate drug development in this disease.
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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 | 0.396 | 0.479 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.013 | 0.007 |
| Research integrity | 0.020 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".