The management of BCG failure in non-muscle-invasive bladder cancer: an update
Bibliographic record
Abstract
Up to 40% of patients with non-muscle-invasive bladder cancer (NMIBC) will fail intravesical bacillus Calmette-Guérin (BCG) therapy. There is unfortunately no current gold standard for salvage intravesical therapy after appropriate BCG treatment. Indeed, outcomes are at best suboptimal. The vast majority of low-grade NMIBC are prone to recur but very rarely progress. Failure after intravesical BCG in these patients is usually superficial and low-grade. At the other end of the spectrum, failure to respond to BCG in high-risk T1 bladder cancer and/or carcinoma in situ (CIS or TIS) is more problematic, since those tumours often have the potential to progress to muscle invasion. In these cases, radical cystectomy remains the mainstay after BCG failure. With appropriate selection, certain patients who "fail" BCG (but with favourable risk factors) can be managed with intravesical regimens, including repeated BCG, BCG plus cytokines, intravesical chemotherapy, thermochemotherapy or new immunotherapeutic modalities. In this review, reasons explaining BCG failure, how to define BCG failure, optimal risk stratification and prediction of response and management of BCG failures are discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".