On the Horizon: A Global Multidisciplinary Perspective on Delivering Emerging Therapies for Patients with BCG-Naïve High-Risk NMIBC
Bibliographic record
Abstract
Patients with high-risk non-muscle invasive bladder cancer (NMIBC) are generally treated with transurethral resection of the bladder tumor followed by intravesical bacillus Calmette-Guérin (BCG), the current standard of care. However, recurrence or progression is common and may result in patients requiring radical cystectomy. Additionally, BCG continues to be in short supply worldwide. Therefore, there is an unmet need for new therapies that provide durable disease control and maintain quality of life. In the BCG-naïve high-risk NMIBC setting, potential new treatment options are emerging, with several regimens combining intravesical therapy with systemic PD-1 or PD-L1-directed immune checkpoint inhibitors (ICIs) currently under investigation in several Phase 3 trials. In routine clinical practice, NMIBC has traditionally been managed almost entirely by urologists. However, the introduction of systemic ICIs would likely require medical oncology expertise to help assess patients' fitness for these therapies and potentially for treatment administration and immune-related adverse event management. While multidisciplinary workflows are common practice for advanced bladder cancer, they would represent a paradigm shift in NMIBC. Based on current experience of managing patients with NMIBC across different countries and healthcare systems from our perspective as urologists, medical oncologists, and nurses, we discuss best practices for the potential integration of emerging therapies such as ICIs into the treatment of BCG-naïve high-risk NMIBC. We emphasize the need for multidisciplinary care, either through formalized multidisciplinary teams or cross-discipline collaborative workflows adapted to local needs, to ensure efficient coordination and sharing of responsibilities. Specialized nurses have the potential to play key roles across multiple aspects of patient care. We also highlight the crucial importance of effective communication across teams, increases in resourcing, and education for healthcare professionals, patients, and caregivers to enable eligible patients with high-risk NMIBC to benefit optimally from the introduction of these potential new treatment options. Supplementary file2 (MP4 407382 kb).
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".