Systemic therapy in bladder preservation
Bibliographic record
Abstract
Bladder cancer is an aggressive and lethal disease. Even when presenting as localized muscle-invasive disease, the 5-year survival rate is about 70%, and the recurrence rate after radical cystectomy is approximately 50%. Neoadjuvant chemotherapy (NAC) has the potential to downstage the primary tumor and treat micrometastases, leading to a decrease in recurrence rates and an increase in cure rates. There is level 1 evidence in favor of neoadjuvant cisplatin-based chemotherapy prior to radical cystectomy. However, data from clinical trials evaluating NAC for patients undergoing bladder-sparing treatments are less robust, so this strategy remains controversial. The response to NAC is prognostic and patients with favorable pathological response have better overall survival. Strategies to select patients based on molecular biomarkers have the potential to guide treatment decisions and even de-intensify treatment, avoiding local treatment for those with complete responses to systemic therapy. This review outlines the current literature on the use of NAC in the context of bladder preservation for muscle-invasive bladder cancer, highlights neoadjuvant studies in patients ineligible for cisplatin-based NAC, and discusses novel bladder-preservation strategies, including multimodality combinations and biomarker-driven studies of definitive chemotherapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".